{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('./credit-a.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = data.iloc[:,:-1].values\n",
    "y = data.iloc[:,-1].replace(-1,0).values.reshape(-1,1)\n",
    "x_train = x[:int(len(x)*0.75)]\n",
    "x_test =  x[int(len(x)*0.75):]\n",
    "y_train= y[:int(len(y)*0.75)]\n",
    "y_test= y[int(len(y)*0.75):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Dense(128,kernel_regularizer=keras.regularizers.l2(0.002),\n",
    "                             input_shape=(x.shape[1],),activation='relu'))\n",
    "model.add(keras.layers.Dense(128,kernel_regularizer=keras.regularizers.l2(0.002),activation='relu'))\n",
    "model.add(keras.layers.Dense(128,kernel_regularizer=keras.regularizers.l2(0.002),activation='relu'))\n",
    "model.add(keras.layers.Dense(1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 489 samples, validate on 164 samples\n",
      "Epoch 1/1000\n",
      "489/489 [==============================] - 1s 1ms/step - loss: 2.9717 - acc: 0.5706 - val_loss: 2.9544 - val_acc: 0.4329\n",
      "Epoch 2/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 2.2912 - acc: 0.6667 - val_loss: 3.0461 - val_acc: 0.3354\n",
      "Epoch 3/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 2.2819 - acc: 0.6585 - val_loss: 4.1245 - val_acc: 0.6280\n",
      "Epoch 4/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 4.6642 - acc: 0.5112 - val_loss: 3.8767 - val_acc: 0.6037\n",
      "Epoch 5/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 2.5712 - acc: 0.6524 - val_loss: 2.6503 - val_acc: 0.6890\n",
      "Epoch 6/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 2.3798 - acc: 0.6503 - val_loss: 3.4066 - val_acc: 0.7134\n",
      "Epoch 7/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 2.6071 - acc: 0.6892 - val_loss: 3.1350 - val_acc: 0.6951\n",
      "Epoch 8/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 2.1791 - acc: 0.6973 - val_loss: 2.7193 - val_acc: 0.6280\n",
      "Epoch 9/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 1.9343 - acc: 0.7444 - val_loss: 2.5426 - val_acc: 0.6768\n",
      "Epoch 10/1000\n",
      "489/489 [==============================] - 0s 177us/step - loss: 2.0125 - acc: 0.7321 - val_loss: 4.4296 - val_acc: 0.3902\n",
      "Epoch 11/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 4.4719 - acc: 0.5787 - val_loss: 3.8488 - val_acc: 0.6037\n",
      "Epoch 12/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 3.9747 - acc: 0.5930 - val_loss: 3.2604 - val_acc: 0.3902\n",
      "Epoch 13/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 2.4464 - acc: 0.6646 - val_loss: 3.1128 - val_acc: 0.6951\n",
      "Epoch 14/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 2.2922 - acc: 0.7178 - val_loss: 2.6339 - val_acc: 0.6524\n",
      "Epoch 15/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 1.9136 - acc: 0.7464 - val_loss: 2.3472 - val_acc: 0.7195\n",
      "Epoch 16/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 1.5621 - acc: 0.7648 - val_loss: 1.3981 - val_acc: 0.7134\n",
      "Epoch 17/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 1.4141 - acc: 0.7301 - val_loss: 1.4091 - val_acc: 0.7317\n",
      "Epoch 18/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 1.6146 - acc: 0.7137 - val_loss: 4.0500 - val_acc: 0.4024\n",
      "Epoch 19/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 2.0827 - acc: 0.7117 - val_loss: 0.9677 - val_acc: 0.7561\n",
      "Epoch 20/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 1.6561 - acc: 0.7526 - val_loss: 2.1211 - val_acc: 0.7195\n",
      "Epoch 21/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 1.3726 - acc: 0.7342 - val_loss: 1.1421 - val_acc: 0.7378\n",
      "Epoch 22/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 1.7367 - acc: 0.6871 - val_loss: 1.6405 - val_acc: 0.7012\n",
      "Epoch 23/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 2.1423 - acc: 0.6483 - val_loss: 3.2166 - val_acc: 0.7012\n",
      "Epoch 24/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 2.5342 - acc: 0.7157 - val_loss: 3.1377 - val_acc: 0.4695\n",
      "Epoch 25/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 2.0906 - acc: 0.7117 - val_loss: 2.5594 - val_acc: 0.7378\n",
      "Epoch 26/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 1.9306 - acc: 0.7342 - val_loss: 2.4554 - val_acc: 0.7134\n",
      "Epoch 27/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 1.7551 - acc: 0.7771 - val_loss: 1.4980 - val_acc: 0.7622\n",
      "Epoch 28/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 1.6209 - acc: 0.7505 - val_loss: 1.9047 - val_acc: 0.7317\n",
      "Epoch 29/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 1.5618 - acc: 0.7157 - val_loss: 1.6674 - val_acc: 0.7256\n",
      "Epoch 30/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 1.4411 - acc: 0.7239 - val_loss: 3.3287 - val_acc: 0.4085\n",
      "Epoch 31/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 2.1636 - acc: 0.7157 - val_loss: 2.8665 - val_acc: 0.7073\n",
      "Epoch 32/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 1.9894 - acc: 0.7485 - val_loss: 2.4553 - val_acc: 0.7195\n",
      "Epoch 33/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 1.8073 - acc: 0.7546 - val_loss: 2.0888 - val_acc: 0.7317\n",
      "Epoch 34/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 1.4535 - acc: 0.7791 - val_loss: 3.1348 - val_acc: 0.6646\n",
      "Epoch 35/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 1.5616 - acc: 0.7628 - val_loss: 1.4905 - val_acc: 0.7561\n",
      "Epoch 36/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 1.9488 - acc: 0.7321 - val_loss: 2.1765 - val_acc: 0.6768\n",
      "Epoch 37/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 1.6584 - acc: 0.7751 - val_loss: 1.9627 - val_acc: 0.7256\n",
      "Epoch 38/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 1.3985 - acc: 0.7730 - val_loss: 1.3891 - val_acc: 0.7561\n",
      "Epoch 39/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 1.0960 - acc: 0.7853 - val_loss: 1.1519 - val_acc: 0.7134\n",
      "Epoch 40/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 1.1040 - acc: 0.7710 - val_loss: 1.1880 - val_acc: 0.7500\n",
      "Epoch 41/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 1.0412 - acc: 0.7669 - val_loss: 1.4195 - val_acc: 0.7439\n",
      "Epoch 42/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 1.2301 - acc: 0.8057 - val_loss: 1.1419 - val_acc: 0.7378\n",
      "Epoch 43/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.8897 - acc: 0.8057 - val_loss: 0.9273 - val_acc: 0.7927\n",
      "Epoch 44/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.9580 - acc: 0.7894 - val_loss: 1.3445 - val_acc: 0.7500\n",
      "Epoch 45/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 1.7741 - acc: 0.7444 - val_loss: 2.6031 - val_acc: 0.7134\n",
      "Epoch 46/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 1.6633 - acc: 0.7730 - val_loss: 1.6323 - val_acc: 0.7561\n",
      "Epoch 47/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 1.6004 - acc: 0.7607 - val_loss: 2.1122 - val_acc: 0.7134\n",
      "Epoch 48/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 1.4889 - acc: 0.7935 - val_loss: 1.7023 - val_acc: 0.7927\n",
      "Epoch 49/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 1.3217 - acc: 0.7935 - val_loss: 1.2875 - val_acc: 0.7805\n",
      "Epoch 50/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 1.3687 - acc: 0.7955 - val_loss: 1.3175 - val_acc: 0.7744\n",
      "Epoch 51/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 1.3645 - acc: 0.7669 - val_loss: 1.4709 - val_acc: 0.7561\n",
      "Epoch 52/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 1.0746 - acc: 0.7526 - val_loss: 1.1806 - val_acc: 0.6463\n",
      "Epoch 53/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.9548 - acc: 0.8139 - val_loss: 1.0364 - val_acc: 0.7500\n",
      "Epoch 54/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 1.1054 - acc: 0.7812 - val_loss: 1.2575 - val_acc: 0.7927\n",
      "Epoch 55/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 1.0608 - acc: 0.8098 - val_loss: 1.1193 - val_acc: 0.7500\n",
      "Epoch 56/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.9504 - acc: 0.7955 - val_loss: 1.3197 - val_acc: 0.6646\n",
      "Epoch 57/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 1.0043 - acc: 0.7975 - val_loss: 1.2784 - val_acc: 0.7439\n",
      "Epoch 58/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 1.0259 - acc: 0.7975 - val_loss: 1.2064 - val_acc: 0.7378\n",
      "Epoch 59/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 1.0804 - acc: 0.7730 - val_loss: 1.6901 - val_acc: 0.6585\n",
      "Epoch 60/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 149us/step - loss: 1.1322 - acc: 0.7975 - val_loss: 1.3405 - val_acc: 0.6951\n",
      "Epoch 61/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.9141 - acc: 0.8119 - val_loss: 0.9354 - val_acc: 0.7683\n",
      "Epoch 62/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.7810 - acc: 0.8057 - val_loss: 0.9186 - val_acc: 0.7561\n",
      "Epoch 63/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.8304 - acc: 0.8221 - val_loss: 1.2850 - val_acc: 0.7378\n",
      "Epoch 64/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 1.0301 - acc: 0.7975 - val_loss: 1.0000 - val_acc: 0.7805\n",
      "Epoch 65/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.7969 - acc: 0.8160 - val_loss: 1.0771 - val_acc: 0.7317\n",
      "Epoch 66/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.8154 - acc: 0.8078 - val_loss: 1.0852 - val_acc: 0.7500\n",
      "Epoch 67/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.8606 - acc: 0.7853 - val_loss: 1.1292 - val_acc: 0.6585\n",
      "Epoch 68/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.8277 - acc: 0.7935 - val_loss: 0.9567 - val_acc: 0.7439\n",
      "Epoch 69/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.7856 - acc: 0.8282 - val_loss: 1.0581 - val_acc: 0.7622\n",
      "Epoch 70/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.7729 - acc: 0.8262 - val_loss: 0.9375 - val_acc: 0.7927\n",
      "Epoch 71/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.7377 - acc: 0.8323 - val_loss: 0.9550 - val_acc: 0.7927\n",
      "Epoch 72/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.7517 - acc: 0.8221 - val_loss: 0.9499 - val_acc: 0.7561\n",
      "Epoch 73/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.7317 - acc: 0.8466 - val_loss: 0.9237 - val_acc: 0.8049\n",
      "Epoch 74/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.7408 - acc: 0.8160 - val_loss: 0.9270 - val_acc: 0.7866\n",
      "Epoch 75/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.7461 - acc: 0.8221 - val_loss: 0.9831 - val_acc: 0.7195\n",
      "Epoch 76/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.7497 - acc: 0.8241 - val_loss: 1.0446 - val_acc: 0.7195\n",
      "Epoch 77/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.9388 - acc: 0.8037 - val_loss: 1.0666 - val_acc: 0.7500\n",
      "Epoch 78/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.8321 - acc: 0.7832 - val_loss: 0.9354 - val_acc: 0.7622\n",
      "Epoch 79/1000\n",
      "489/489 [==============================] - 0s 184us/step - loss: 0.7668 - acc: 0.8241 - val_loss: 1.0419 - val_acc: 0.7256\n",
      "Epoch 80/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.8089 - acc: 0.8180 - val_loss: 1.0723 - val_acc: 0.7134\n",
      "Epoch 81/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.8997 - acc: 0.7607 - val_loss: 0.9114 - val_acc: 0.7622\n",
      "Epoch 82/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.7297 - acc: 0.8078 - val_loss: 0.9169 - val_acc: 0.7805\n",
      "Epoch 83/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.7336 - acc: 0.8200 - val_loss: 0.8883 - val_acc: 0.8171\n",
      "Epoch 84/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.7345 - acc: 0.8200 - val_loss: 0.9778 - val_acc: 0.7744\n",
      "Epoch 85/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.7080 - acc: 0.8282 - val_loss: 1.2926 - val_acc: 0.7012\n",
      "Epoch 86/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 1.0580 - acc: 0.7914 - val_loss: 1.1043 - val_acc: 0.7012\n",
      "Epoch 87/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 1.1032 - acc: 0.7587 - val_loss: 0.9014 - val_acc: 0.7683\n",
      "Epoch 88/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.8235 - acc: 0.7975 - val_loss: 1.1659 - val_acc: 0.6768\n",
      "Epoch 89/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.9095 - acc: 0.7832 - val_loss: 1.0240 - val_acc: 0.7256\n",
      "Epoch 90/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.8350 - acc: 0.8016 - val_loss: 0.8769 - val_acc: 0.7927\n",
      "Epoch 91/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.7121 - acc: 0.8160 - val_loss: 0.9677 - val_acc: 0.7866\n",
      "Epoch 92/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.7293 - acc: 0.8180 - val_loss: 1.0158 - val_acc: 0.7866\n",
      "Epoch 93/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.8453 - acc: 0.7853 - val_loss: 0.9603 - val_acc: 0.7195\n",
      "Epoch 94/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.7094 - acc: 0.8200 - val_loss: 0.9086 - val_acc: 0.7561\n",
      "Epoch 95/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.6603 - acc: 0.8446 - val_loss: 0.8548 - val_acc: 0.7744\n",
      "Epoch 96/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.6449 - acc: 0.8282 - val_loss: 0.8448 - val_acc: 0.7805\n",
      "Epoch 97/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.6604 - acc: 0.8323 - val_loss: 0.9460 - val_acc: 0.7683\n",
      "Epoch 98/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.6948 - acc: 0.8037 - val_loss: 0.8779 - val_acc: 0.8110\n",
      "Epoch 99/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.8707 - acc: 0.8016 - val_loss: 1.0070 - val_acc: 0.7195\n",
      "Epoch 100/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.7226 - acc: 0.8037 - val_loss: 1.1880 - val_acc: 0.6890\n",
      "Epoch 101/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.6978 - acc: 0.8098 - val_loss: 0.8950 - val_acc: 0.8293\n",
      "Epoch 102/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.6688 - acc: 0.7935 - val_loss: 0.8660 - val_acc: 0.7805\n",
      "Epoch 103/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.6966 - acc: 0.8160 - val_loss: 0.8503 - val_acc: 0.7866\n",
      "Epoch 104/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.6497 - acc: 0.8262 - val_loss: 0.8797 - val_acc: 0.7683\n",
      "Epoch 105/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.6846 - acc: 0.8098 - val_loss: 0.9090 - val_acc: 0.7073\n",
      "Epoch 106/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.6728 - acc: 0.8364 - val_loss: 0.9026 - val_acc: 0.8049\n",
      "Epoch 107/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.6332 - acc: 0.8528 - val_loss: 0.8751 - val_acc: 0.7378\n",
      "Epoch 108/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.6176 - acc: 0.8323 - val_loss: 0.8418 - val_acc: 0.8171\n",
      "Epoch 109/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.6170 - acc: 0.8528 - val_loss: 0.8572 - val_acc: 0.8110\n",
      "Epoch 110/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.7553 - acc: 0.8200 - val_loss: 0.8394 - val_acc: 0.7927\n",
      "Epoch 111/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.6499 - acc: 0.8221 - val_loss: 0.8529 - val_acc: 0.7317\n",
      "Epoch 112/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.6368 - acc: 0.8384 - val_loss: 0.8791 - val_acc: 0.7439\n",
      "Epoch 113/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.6580 - acc: 0.8221 - val_loss: 0.9015 - val_acc: 0.7073\n",
      "Epoch 114/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.6640 - acc: 0.8425 - val_loss: 0.8709 - val_acc: 0.7988\n",
      "Epoch 115/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.6220 - acc: 0.8323 - val_loss: 0.8409 - val_acc: 0.7683\n",
      "Epoch 116/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.6814 - acc: 0.8221 - val_loss: 0.8540 - val_acc: 0.8049\n",
      "Epoch 117/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.6141 - acc: 0.8548 - val_loss: 0.9132 - val_acc: 0.7256\n",
      "Epoch 118/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.7304 - acc: 0.8016 - val_loss: 0.8863 - val_acc: 0.7195\n",
      "Epoch 119/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.6271 - acc: 0.8364 - val_loss: 0.9307 - val_acc: 0.6585\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.6594 - acc: 0.8221 - val_loss: 0.9293 - val_acc: 0.6768\n",
      "Epoch 121/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.6313 - acc: 0.8221 - val_loss: 0.8475 - val_acc: 0.7805\n",
      "Epoch 122/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5967 - acc: 0.8262 - val_loss: 0.9358 - val_acc: 0.6707\n",
      "Epoch 123/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.6414 - acc: 0.7975 - val_loss: 0.8836 - val_acc: 0.7500\n",
      "Epoch 124/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.6173 - acc: 0.8405 - val_loss: 0.9109 - val_acc: 0.6768\n",
      "Epoch 125/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.6336 - acc: 0.8180 - val_loss: 0.8108 - val_acc: 0.7744\n",
      "Epoch 126/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.5975 - acc: 0.8364 - val_loss: 0.8400 - val_acc: 0.7561\n",
      "Epoch 127/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.6248 - acc: 0.8364 - val_loss: 0.8563 - val_acc: 0.8110\n",
      "Epoch 128/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.6427 - acc: 0.8323 - val_loss: 0.9054 - val_acc: 0.7683\n",
      "Epoch 129/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.6240 - acc: 0.8425 - val_loss: 0.8703 - val_acc: 0.7500\n",
      "Epoch 130/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.6394 - acc: 0.8139 - val_loss: 0.8592 - val_acc: 0.7195\n",
      "Epoch 131/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.6176 - acc: 0.8262 - val_loss: 0.8515 - val_acc: 0.7378\n",
      "Epoch 132/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.6776 - acc: 0.8262 - val_loss: 0.8846 - val_acc: 0.7683\n",
      "Epoch 133/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.6395 - acc: 0.8078 - val_loss: 0.8291 - val_acc: 0.7439\n",
      "Epoch 134/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.6040 - acc: 0.8323 - val_loss: 0.7904 - val_acc: 0.7500\n",
      "Epoch 135/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.6418 - acc: 0.8057 - val_loss: 0.8093 - val_acc: 0.8049\n",
      "Epoch 136/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.6203 - acc: 0.8221 - val_loss: 0.8062 - val_acc: 0.7744\n",
      "Epoch 137/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.6106 - acc: 0.8446 - val_loss: 0.8219 - val_acc: 0.7134\n",
      "Epoch 138/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.5882 - acc: 0.8303 - val_loss: 0.8379 - val_acc: 0.7561\n",
      "Epoch 139/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.6101 - acc: 0.8446 - val_loss: 0.8192 - val_acc: 0.7500\n",
      "Epoch 140/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.7775 - acc: 0.7975 - val_loss: 1.0437 - val_acc: 0.7012\n",
      "Epoch 141/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.7903 - acc: 0.7791 - val_loss: 0.8265 - val_acc: 0.7866\n",
      "Epoch 142/1000\n",
      "489/489 [==============================] - 0s 122us/step - loss: 0.6521 - acc: 0.7873 - val_loss: 0.8133 - val_acc: 0.8110\n",
      "Epoch 143/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.7163 - acc: 0.8098 - val_loss: 0.8191 - val_acc: 0.8049\n",
      "Epoch 144/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.6002 - acc: 0.8446 - val_loss: 0.8493 - val_acc: 0.7500\n",
      "Epoch 145/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5904 - acc: 0.8405 - val_loss: 0.8057 - val_acc: 0.7927\n",
      "Epoch 146/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.5807 - acc: 0.8446 - val_loss: 0.7936 - val_acc: 0.7988\n",
      "Epoch 147/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.5697 - acc: 0.8466 - val_loss: 0.7983 - val_acc: 0.7927\n",
      "Epoch 148/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.5647 - acc: 0.8507 - val_loss: 0.7793 - val_acc: 0.8110\n",
      "Epoch 149/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.5610 - acc: 0.8487 - val_loss: 0.9795 - val_acc: 0.7256\n",
      "Epoch 150/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.9659 - acc: 0.7975 - val_loss: 0.8155 - val_acc: 0.7866\n",
      "Epoch 151/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.7800 - acc: 0.8221 - val_loss: 0.9359 - val_acc: 0.7683\n",
      "Epoch 152/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.7294 - acc: 0.8262 - val_loss: 0.8185 - val_acc: 0.7988\n",
      "Epoch 153/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.6015 - acc: 0.8200 - val_loss: 0.8353 - val_acc: 0.7622\n",
      "Epoch 154/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.5948 - acc: 0.8425 - val_loss: 0.7761 - val_acc: 0.7744\n",
      "Epoch 155/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.5727 - acc: 0.8569 - val_loss: 0.8129 - val_acc: 0.7683\n",
      "Epoch 156/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.5545 - acc: 0.8548 - val_loss: 0.7906 - val_acc: 0.7622\n",
      "Epoch 157/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.5519 - acc: 0.8507 - val_loss: 0.7782 - val_acc: 0.7500\n",
      "Epoch 158/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.5548 - acc: 0.8446 - val_loss: 0.7794 - val_acc: 0.7500\n",
      "Epoch 159/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.5472 - acc: 0.8548 - val_loss: 0.8211 - val_acc: 0.7744\n",
      "Epoch 160/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5818 - acc: 0.8344 - val_loss: 0.8166 - val_acc: 0.7073\n",
      "Epoch 161/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.5474 - acc: 0.8466 - val_loss: 0.7760 - val_acc: 0.7500\n",
      "Epoch 162/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.5434 - acc: 0.8630 - val_loss: 0.8477 - val_acc: 0.6890\n",
      "Epoch 163/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5497 - acc: 0.8364 - val_loss: 0.7675 - val_acc: 0.7866\n",
      "Epoch 164/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.5344 - acc: 0.8691 - val_loss: 0.7984 - val_acc: 0.7378\n",
      "Epoch 165/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5537 - acc: 0.8384 - val_loss: 0.7704 - val_acc: 0.7561\n",
      "Epoch 166/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5607 - acc: 0.8405 - val_loss: 0.7957 - val_acc: 0.7866\n",
      "Epoch 167/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5472 - acc: 0.8507 - val_loss: 0.7713 - val_acc: 0.7805\n",
      "Epoch 168/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5268 - acc: 0.8487 - val_loss: 0.8078 - val_acc: 0.7195\n",
      "Epoch 169/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.5648 - acc: 0.8609 - val_loss: 0.8814 - val_acc: 0.6890\n",
      "Epoch 170/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.6189 - acc: 0.8098 - val_loss: 0.8010 - val_acc: 0.7500\n",
      "Epoch 171/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.5714 - acc: 0.8262 - val_loss: 0.8276 - val_acc: 0.7134\n",
      "Epoch 172/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.5940 - acc: 0.8262 - val_loss: 0.8207 - val_acc: 0.7988\n",
      "Epoch 173/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5821 - acc: 0.8364 - val_loss: 0.7583 - val_acc: 0.7744\n",
      "Epoch 174/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.5610 - acc: 0.8487 - val_loss: 0.7417 - val_acc: 0.7866\n",
      "Epoch 175/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.5807 - acc: 0.8548 - val_loss: 0.7624 - val_acc: 0.8232\n",
      "Epoch 176/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.8935 - acc: 0.8057 - val_loss: 1.3000 - val_acc: 0.6951\n",
      "Epoch 177/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.9654 - acc: 0.7873 - val_loss: 1.2018 - val_acc: 0.7073\n",
      "Epoch 178/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 1.2185 - acc: 0.8057 - val_loss: 1.1173 - val_acc: 0.7317\n",
      "Epoch 179/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 137us/step - loss: 0.7911 - acc: 0.8098 - val_loss: 0.8653 - val_acc: 0.7622\n",
      "Epoch 180/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5780 - acc: 0.8487 - val_loss: 0.8037 - val_acc: 0.8110\n",
      "Epoch 181/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.5494 - acc: 0.8507 - val_loss: 0.7935 - val_acc: 0.7561\n",
      "Epoch 182/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.5283 - acc: 0.8589 - val_loss: 0.8416 - val_acc: 0.6951\n",
      "Epoch 183/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.5671 - acc: 0.8425 - val_loss: 0.8030 - val_acc: 0.7683\n",
      "Epoch 184/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.5525 - acc: 0.8425 - val_loss: 0.7964 - val_acc: 0.7500\n",
      "Epoch 185/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.5475 - acc: 0.8528 - val_loss: 0.7787 - val_acc: 0.7744\n",
      "Epoch 186/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.5128 - acc: 0.8589 - val_loss: 0.7726 - val_acc: 0.7744\n",
      "Epoch 187/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5054 - acc: 0.8609 - val_loss: 0.7952 - val_acc: 0.7561\n",
      "Epoch 188/1000\n",
      "489/489 [==============================] - 0s 177us/step - loss: 0.5776 - acc: 0.8384 - val_loss: 0.7959 - val_acc: 0.7195\n",
      "Epoch 189/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.5447 - acc: 0.8528 - val_loss: 0.7960 - val_acc: 0.7622\n",
      "Epoch 190/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5161 - acc: 0.8528 - val_loss: 0.9016 - val_acc: 0.6768\n",
      "Epoch 191/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4939 - acc: 0.8732 - val_loss: 0.7982 - val_acc: 0.7805\n",
      "Epoch 192/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.4835 - acc: 0.8712 - val_loss: 0.7808 - val_acc: 0.7805\n",
      "Epoch 193/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4746 - acc: 0.8753 - val_loss: 0.8332 - val_acc: 0.7195\n",
      "Epoch 194/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5021 - acc: 0.8691 - val_loss: 0.8753 - val_acc: 0.7134\n",
      "Epoch 195/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.5052 - acc: 0.8630 - val_loss: 0.7524 - val_acc: 0.7927\n",
      "Epoch 196/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.5324 - acc: 0.8384 - val_loss: 0.8559 - val_acc: 0.7622\n",
      "Epoch 197/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.5055 - acc: 0.8671 - val_loss: 0.8271 - val_acc: 0.7866\n",
      "Epoch 198/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4895 - acc: 0.8671 - val_loss: 0.7998 - val_acc: 0.7744\n",
      "Epoch 199/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4767 - acc: 0.8855 - val_loss: 0.7833 - val_acc: 0.7988\n",
      "Epoch 200/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.4890 - acc: 0.8650 - val_loss: 0.8177 - val_acc: 0.8049\n",
      "Epoch 201/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.5252 - acc: 0.8528 - val_loss: 0.7889 - val_acc: 0.7683\n",
      "Epoch 202/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.5036 - acc: 0.8793 - val_loss: 0.7403 - val_acc: 0.7805\n",
      "Epoch 203/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4998 - acc: 0.8671 - val_loss: 0.8910 - val_acc: 0.6951\n",
      "Epoch 204/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4906 - acc: 0.8650 - val_loss: 0.8188 - val_acc: 0.8354\n",
      "Epoch 205/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.5014 - acc: 0.8691 - val_loss: 0.8029 - val_acc: 0.7317\n",
      "Epoch 206/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.5015 - acc: 0.8609 - val_loss: 0.8716 - val_acc: 0.7317\n",
      "Epoch 207/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.5533 - acc: 0.8589 - val_loss: 0.8148 - val_acc: 0.7561\n",
      "Epoch 208/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.5479 - acc: 0.8303 - val_loss: 0.7306 - val_acc: 0.8049\n",
      "Epoch 209/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5138 - acc: 0.8773 - val_loss: 0.7251 - val_acc: 0.8110\n",
      "Epoch 210/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.5441 - acc: 0.8528 - val_loss: 0.7505 - val_acc: 0.7866\n",
      "Epoch 211/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4791 - acc: 0.8753 - val_loss: 0.7346 - val_acc: 0.7561\n",
      "Epoch 212/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4764 - acc: 0.8814 - val_loss: 0.7725 - val_acc: 0.7561\n",
      "Epoch 213/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5734 - acc: 0.8446 - val_loss: 0.7615 - val_acc: 0.7988\n",
      "Epoch 214/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.5254 - acc: 0.8753 - val_loss: 1.0165 - val_acc: 0.6890\n",
      "Epoch 215/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.5326 - acc: 0.8650 - val_loss: 0.8813 - val_acc: 0.7073\n",
      "Epoch 216/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.5031 - acc: 0.8589 - val_loss: 0.7770 - val_acc: 0.7805\n",
      "Epoch 217/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4684 - acc: 0.8609 - val_loss: 0.7783 - val_acc: 0.7683\n",
      "Epoch 218/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4634 - acc: 0.8671 - val_loss: 0.7896 - val_acc: 0.7439\n",
      "Epoch 219/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4494 - acc: 0.8793 - val_loss: 0.9184 - val_acc: 0.6829\n",
      "Epoch 220/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5251 - acc: 0.8753 - val_loss: 0.9077 - val_acc: 0.7134\n",
      "Epoch 221/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.5623 - acc: 0.8405 - val_loss: 0.8722 - val_acc: 0.7256\n",
      "Epoch 222/1000\n",
      "489/489 [==============================] - 0s 177us/step - loss: 0.8234 - acc: 0.8262 - val_loss: 1.0509 - val_acc: 0.7744\n",
      "Epoch 223/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.6472 - acc: 0.8364 - val_loss: 0.8344 - val_acc: 0.7439\n",
      "Epoch 224/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.5719 - acc: 0.8364 - val_loss: 0.8561 - val_acc: 0.7195\n",
      "Epoch 225/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4939 - acc: 0.8507 - val_loss: 0.8717 - val_acc: 0.7134\n",
      "Epoch 226/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.5067 - acc: 0.8425 - val_loss: 0.9002 - val_acc: 0.6951\n",
      "Epoch 227/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.5103 - acc: 0.8282 - val_loss: 0.9138 - val_acc: 0.7317\n",
      "Epoch 228/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4915 - acc: 0.8507 - val_loss: 0.8238 - val_acc: 0.7927\n",
      "Epoch 229/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.4763 - acc: 0.8630 - val_loss: 0.8531 - val_acc: 0.7439\n",
      "Epoch 230/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5072 - acc: 0.8446 - val_loss: 0.7996 - val_acc: 0.7805\n",
      "Epoch 231/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.5027 - acc: 0.8405 - val_loss: 0.8178 - val_acc: 0.7622\n",
      "Epoch 232/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.5174 - acc: 0.8200 - val_loss: 0.8165 - val_acc: 0.7927\n",
      "Epoch 233/1000\n",
      "489/489 [==============================] - ETA: 0s - loss: 0.4184 - acc: 0.875 - 0s 131us/step - loss: 0.4827 - acc: 0.8732 - val_loss: 0.8224 - val_acc: 0.7256\n",
      "Epoch 234/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4797 - acc: 0.8691 - val_loss: 0.8021 - val_acc: 0.7378\n",
      "Epoch 235/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4567 - acc: 0.8793 - val_loss: 0.7611 - val_acc: 0.7561\n",
      "Epoch 236/1000\n",
      "489/489 [==============================] - 0s 179us/step - loss: 0.5027 - acc: 0.8712 - val_loss: 0.8092 - val_acc: 0.7622\n",
      "Epoch 237/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4877 - acc: 0.8630 - val_loss: 0.8167 - val_acc: 0.7439\n",
      "Epoch 238/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 139us/step - loss: 0.4828 - acc: 0.8589 - val_loss: 0.8611 - val_acc: 0.6829\n",
      "Epoch 239/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4872 - acc: 0.8589 - val_loss: 0.7932 - val_acc: 0.7744\n",
      "Epoch 240/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.5259 - acc: 0.8200 - val_loss: 0.7859 - val_acc: 0.6951\n",
      "Epoch 241/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.5048 - acc: 0.8548 - val_loss: 0.8190 - val_acc: 0.7134\n",
      "Epoch 242/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4860 - acc: 0.8589 - val_loss: 0.8040 - val_acc: 0.7439\n",
      "Epoch 243/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4730 - acc: 0.8630 - val_loss: 0.7742 - val_acc: 0.7500\n",
      "Epoch 244/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.4606 - acc: 0.8814 - val_loss: 0.8370 - val_acc: 0.6585\n",
      "Epoch 245/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4722 - acc: 0.8405 - val_loss: 0.8443 - val_acc: 0.6829\n",
      "Epoch 246/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.5423 - acc: 0.7607 - val_loss: 0.8773 - val_acc: 0.6463\n",
      "Epoch 247/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.5343 - acc: 0.8466 - val_loss: 0.7926 - val_acc: 0.7439\n",
      "Epoch 248/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.5301 - acc: 0.8630 - val_loss: 0.8462 - val_acc: 0.6890\n",
      "Epoch 249/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.5102 - acc: 0.8650 - val_loss: 0.8302 - val_acc: 0.7134\n",
      "Epoch 250/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.5263 - acc: 0.8405 - val_loss: 0.9307 - val_acc: 0.6524\n",
      "Epoch 251/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.4935 - acc: 0.8507 - val_loss: 0.7933 - val_acc: 0.7073\n",
      "Epoch 252/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.4423 - acc: 0.8712 - val_loss: 0.7722 - val_acc: 0.7195\n",
      "Epoch 253/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4618 - acc: 0.8691 - val_loss: 0.7982 - val_acc: 0.7683\n",
      "Epoch 254/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4931 - acc: 0.8712 - val_loss: 0.9034 - val_acc: 0.7134\n",
      "Epoch 255/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.5356 - acc: 0.8323 - val_loss: 0.8282 - val_acc: 0.7378\n",
      "Epoch 256/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4719 - acc: 0.8609 - val_loss: 1.0420 - val_acc: 0.6829\n",
      "Epoch 257/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.9476 - acc: 0.8507 - val_loss: 0.9920 - val_acc: 0.7988\n",
      "Epoch 258/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.8024 - acc: 0.8221 - val_loss: 0.8298 - val_acc: 0.7256\n",
      "Epoch 259/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.5491 - acc: 0.8364 - val_loss: 0.8500 - val_acc: 0.7195\n",
      "Epoch 260/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4795 - acc: 0.8569 - val_loss: 0.8618 - val_acc: 0.6646\n",
      "Epoch 261/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.4520 - acc: 0.8691 - val_loss: 0.8829 - val_acc: 0.7012\n",
      "Epoch 262/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.4436 - acc: 0.8671 - val_loss: 0.7758 - val_acc: 0.7866\n",
      "Epoch 263/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4369 - acc: 0.8753 - val_loss: 0.8009 - val_acc: 0.7500\n",
      "Epoch 264/1000\n",
      "489/489 [==============================] - 0s 184us/step - loss: 0.4163 - acc: 0.8793 - val_loss: 0.8997 - val_acc: 0.6951\n",
      "Epoch 265/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4498 - acc: 0.8589 - val_loss: 0.8483 - val_acc: 0.7500\n",
      "Epoch 266/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4645 - acc: 0.8753 - val_loss: 0.7495 - val_acc: 0.7317\n",
      "Epoch 267/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.4508 - acc: 0.8671 - val_loss: 0.7985 - val_acc: 0.7256\n",
      "Epoch 268/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.4669 - acc: 0.8507 - val_loss: 0.7737 - val_acc: 0.7988\n",
      "Epoch 269/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4411 - acc: 0.8834 - val_loss: 0.8641 - val_acc: 0.6768\n",
      "Epoch 270/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4512 - acc: 0.8773 - val_loss: 0.8431 - val_acc: 0.7744\n",
      "Epoch 271/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4512 - acc: 0.8671 - val_loss: 0.8892 - val_acc: 0.6707\n",
      "Epoch 272/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.4473 - acc: 0.8671 - val_loss: 0.8255 - val_acc: 0.7195\n",
      "Epoch 273/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4418 - acc: 0.8691 - val_loss: 0.8313 - val_acc: 0.7073\n",
      "Epoch 274/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4336 - acc: 0.8650 - val_loss: 0.8757 - val_acc: 0.6951\n",
      "Epoch 275/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4337 - acc: 0.8753 - val_loss: 0.9253 - val_acc: 0.6646\n",
      "Epoch 276/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4439 - acc: 0.8609 - val_loss: 0.8489 - val_acc: 0.7134\n",
      "Epoch 277/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4420 - acc: 0.8691 - val_loss: 0.8747 - val_acc: 0.7012\n",
      "Epoch 278/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4821 - acc: 0.8160 - val_loss: 0.8252 - val_acc: 0.6768\n",
      "Epoch 279/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.4783 - acc: 0.8671 - val_loss: 0.9305 - val_acc: 0.6585\n",
      "Epoch 280/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4545 - acc: 0.8446 - val_loss: 0.8490 - val_acc: 0.7012\n",
      "Epoch 281/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4647 - acc: 0.8671 - val_loss: 0.8486 - val_acc: 0.6890\n",
      "Epoch 282/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.4330 - acc: 0.8793 - val_loss: 0.8395 - val_acc: 0.7195\n",
      "Epoch 283/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4491 - acc: 0.8814 - val_loss: 0.8052 - val_acc: 0.7378\n",
      "Epoch 284/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4841 - acc: 0.8609 - val_loss: 0.8033 - val_acc: 0.7195\n",
      "Epoch 285/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.5042 - acc: 0.8528 - val_loss: 0.7929 - val_acc: 0.7744\n",
      "Epoch 286/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4859 - acc: 0.8609 - val_loss: 0.7796 - val_acc: 0.7866\n",
      "Epoch 287/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4217 - acc: 0.8773 - val_loss: 0.8130 - val_acc: 0.7561\n",
      "Epoch 288/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4417 - acc: 0.8773 - val_loss: 0.7825 - val_acc: 0.7256\n",
      "Epoch 289/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.4112 - acc: 0.8814 - val_loss: 0.8417 - val_acc: 0.7012\n",
      "Epoch 290/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4550 - acc: 0.8589 - val_loss: 0.8955 - val_acc: 0.6829\n",
      "Epoch 291/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4374 - acc: 0.8589 - val_loss: 0.7814 - val_acc: 0.7927\n",
      "Epoch 292/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4155 - acc: 0.8712 - val_loss: 0.8344 - val_acc: 0.7622\n",
      "Epoch 293/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4370 - acc: 0.8446 - val_loss: 0.7961 - val_acc: 0.7500\n",
      "Epoch 294/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4006 - acc: 0.8875 - val_loss: 0.7688 - val_acc: 0.7744\n",
      "Epoch 295/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4010 - acc: 0.8896 - val_loss: 0.7567 - val_acc: 0.7744\n",
      "Epoch 296/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.4208 - acc: 0.8773 - val_loss: 0.7402 - val_acc: 0.8171\n",
      "Epoch 297/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 163us/step - loss: 0.4217 - acc: 0.8773 - val_loss: 0.7524 - val_acc: 0.7378\n",
      "Epoch 298/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4417 - acc: 0.8712 - val_loss: 0.8347 - val_acc: 0.7988\n",
      "Epoch 299/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4683 - acc: 0.8671 - val_loss: 0.9518 - val_acc: 0.6402\n",
      "Epoch 300/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.4721 - acc: 0.8671 - val_loss: 1.5451 - val_acc: 0.6707\n",
      "Epoch 301/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.6345 - acc: 0.8528 - val_loss: 0.8019 - val_acc: 0.7744\n",
      "Epoch 302/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4330 - acc: 0.8773 - val_loss: 0.7550 - val_acc: 0.7500\n",
      "Epoch 303/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4189 - acc: 0.8814 - val_loss: 0.7861 - val_acc: 0.7561\n",
      "Epoch 304/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4355 - acc: 0.8630 - val_loss: 0.7956 - val_acc: 0.7866\n",
      "Epoch 305/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4044 - acc: 0.8916 - val_loss: 0.8448 - val_acc: 0.7317\n",
      "Epoch 306/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3995 - acc: 0.8896 - val_loss: 0.8370 - val_acc: 0.7317\n",
      "Epoch 307/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.4884 - acc: 0.8364 - val_loss: 0.7592 - val_acc: 0.7988\n",
      "Epoch 308/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4454 - acc: 0.8712 - val_loss: 0.8043 - val_acc: 0.6951\n",
      "Epoch 309/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.8998 - val_acc: 0.6890\n",
      "Epoch 310/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4489 - acc: 0.8650 - val_loss: 0.7744 - val_acc: 0.7622\n",
      "Epoch 311/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4034 - acc: 0.8896 - val_loss: 0.7798 - val_acc: 0.7500\n",
      "Epoch 312/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.4025 - acc: 0.8855 - val_loss: 0.7477 - val_acc: 0.8049\n",
      "Epoch 313/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3954 - acc: 0.8937 - val_loss: 0.8121 - val_acc: 0.7927\n",
      "Epoch 314/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4293 - acc: 0.8753 - val_loss: 0.7309 - val_acc: 0.7927\n",
      "Epoch 315/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4389 - acc: 0.8528 - val_loss: 0.8051 - val_acc: 0.7744\n",
      "Epoch 316/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.4386 - acc: 0.8609 - val_loss: 0.8709 - val_acc: 0.7012\n",
      "Epoch 317/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4004 - acc: 0.8855 - val_loss: 0.9104 - val_acc: 0.7012\n",
      "Epoch 318/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4074 - acc: 0.8753 - val_loss: 0.7945 - val_acc: 0.7378\n",
      "Epoch 319/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4029 - acc: 0.8937 - val_loss: 0.7912 - val_acc: 0.7378\n",
      "Epoch 320/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3821 - acc: 0.8896 - val_loss: 0.8235 - val_acc: 0.7561\n",
      "Epoch 321/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4569 - acc: 0.8139 - val_loss: 0.9151 - val_acc: 0.6768\n",
      "Epoch 322/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.5007 - acc: 0.8650 - val_loss: 0.7454 - val_acc: 0.7439\n",
      "Epoch 323/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4474 - acc: 0.8773 - val_loss: 0.8149 - val_acc: 0.7317\n",
      "Epoch 324/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.4331 - acc: 0.8834 - val_loss: 0.8218 - val_acc: 0.7561\n",
      "Epoch 325/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4469 - acc: 0.8793 - val_loss: 1.1784 - val_acc: 0.6585\n",
      "Epoch 326/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.5005 - acc: 0.8425 - val_loss: 0.8430 - val_acc: 0.7988\n",
      "Epoch 327/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.5417 - acc: 0.8548 - val_loss: 0.8859 - val_acc: 0.7805\n",
      "Epoch 328/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.4832 - acc: 0.8589 - val_loss: 0.8070 - val_acc: 0.7378\n",
      "Epoch 329/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4734 - acc: 0.8630 - val_loss: 0.7979 - val_acc: 0.7378\n",
      "Epoch 330/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4561 - acc: 0.8732 - val_loss: 0.8180 - val_acc: 0.7378\n",
      "Epoch 331/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.4509 - acc: 0.8732 - val_loss: 0.8986 - val_acc: 0.6646\n",
      "Epoch 332/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4517 - acc: 0.8753 - val_loss: 0.7897 - val_acc: 0.7500\n",
      "Epoch 333/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4493 - acc: 0.8671 - val_loss: 0.7863 - val_acc: 0.7378\n",
      "Epoch 334/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.4506 - acc: 0.8569 - val_loss: 0.9412 - val_acc: 0.6585\n",
      "Epoch 335/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4322 - acc: 0.8855 - val_loss: 0.8132 - val_acc: 0.7622\n",
      "Epoch 336/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4523 - acc: 0.8630 - val_loss: 0.8057 - val_acc: 0.7439\n",
      "Epoch 337/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4497 - acc: 0.8609 - val_loss: 0.8762 - val_acc: 0.6768\n",
      "Epoch 338/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4228 - acc: 0.8712 - val_loss: 0.7408 - val_acc: 0.8049\n",
      "Epoch 339/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4759 - acc: 0.8773 - val_loss: 0.7959 - val_acc: 0.7378\n",
      "Epoch 340/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.4160 - acc: 0.8732 - val_loss: 0.7429 - val_acc: 0.8293\n",
      "Epoch 341/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4189 - acc: 0.8773 - val_loss: 0.8613 - val_acc: 0.7195\n",
      "Epoch 342/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4205 - acc: 0.8712 - val_loss: 0.9785 - val_acc: 0.6890\n",
      "Epoch 343/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.4415 - acc: 0.8609 - val_loss: 0.9955 - val_acc: 0.6890\n",
      "Epoch 344/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4233 - acc: 0.8691 - val_loss: 0.8568 - val_acc: 0.7988\n",
      "Epoch 345/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.6671 - acc: 0.8262 - val_loss: 0.7329 - val_acc: 0.8232\n",
      "Epoch 346/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4170 - acc: 0.8855 - val_loss: 0.7920 - val_acc: 0.7378\n",
      "Epoch 347/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4148 - acc: 0.8732 - val_loss: 0.8188 - val_acc: 0.7378\n",
      "Epoch 348/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4189 - acc: 0.8834 - val_loss: 0.7502 - val_acc: 0.7988\n",
      "Epoch 349/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4109 - acc: 0.8875 - val_loss: 0.9251 - val_acc: 0.6646\n",
      "Epoch 350/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3977 - acc: 0.8834 - val_loss: 0.7268 - val_acc: 0.8293\n",
      "Epoch 351/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3950 - acc: 0.8916 - val_loss: 0.8456 - val_acc: 0.6890\n",
      "Epoch 352/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.4047 - acc: 0.8773 - val_loss: 0.8223 - val_acc: 0.7256\n",
      "Epoch 353/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3767 - acc: 0.8978 - val_loss: 0.7836 - val_acc: 0.7927\n",
      "Epoch 354/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3776 - acc: 0.9080 - val_loss: 0.8230 - val_acc: 0.7195\n",
      "Epoch 355/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3603 - acc: 0.9100 - val_loss: 0.7874 - val_acc: 0.7683\n",
      "Epoch 356/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 157us/step - loss: 0.3585 - acc: 0.9080 - val_loss: 0.7954 - val_acc: 0.7744\n",
      "Epoch 357/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3932 - acc: 0.8978 - val_loss: 0.7782 - val_acc: 0.7744\n",
      "Epoch 358/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3890 - acc: 0.8916 - val_loss: 0.7681 - val_acc: 0.7866\n",
      "Epoch 359/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4160 - acc: 0.8855 - val_loss: 0.7275 - val_acc: 0.7988\n",
      "Epoch 360/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3899 - acc: 0.8937 - val_loss: 0.7730 - val_acc: 0.7500\n",
      "Epoch 361/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3603 - acc: 0.9039 - val_loss: 0.7680 - val_acc: 0.7683\n",
      "Epoch 362/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3671 - acc: 0.8937 - val_loss: 0.7180 - val_acc: 0.7927\n",
      "Epoch 363/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4060 - acc: 0.8834 - val_loss: 0.8463 - val_acc: 0.7012\n",
      "Epoch 364/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3856 - acc: 0.8896 - val_loss: 0.7713 - val_acc: 0.7927\n",
      "Epoch 365/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3776 - acc: 0.8937 - val_loss: 0.7114 - val_acc: 0.7927\n",
      "Epoch 366/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3747 - acc: 0.8916 - val_loss: 0.7553 - val_acc: 0.7805\n",
      "Epoch 367/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.3684 - acc: 0.8957 - val_loss: 0.8652 - val_acc: 0.7012\n",
      "Epoch 368/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3846 - acc: 0.8978 - val_loss: 0.7283 - val_acc: 0.7622\n",
      "Epoch 369/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3641 - acc: 0.8957 - val_loss: 0.8982 - val_acc: 0.7012\n",
      "Epoch 370/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3666 - acc: 0.8937 - val_loss: 0.8609 - val_acc: 0.6951\n",
      "Epoch 371/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3950 - acc: 0.8753 - val_loss: 0.8438 - val_acc: 0.7317\n",
      "Epoch 372/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.4039 - acc: 0.8671 - val_loss: 0.7156 - val_acc: 0.8049\n",
      "Epoch 373/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3408 - acc: 0.9039 - val_loss: 0.7331 - val_acc: 0.7744\n",
      "Epoch 374/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.3504 - acc: 0.8937 - val_loss: 0.8063 - val_acc: 0.7561\n",
      "Epoch 375/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3721 - acc: 0.8896 - val_loss: 0.8218 - val_acc: 0.7500\n",
      "Epoch 376/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3853 - acc: 0.8793 - val_loss: 0.8937 - val_acc: 0.7195\n",
      "Epoch 377/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3770 - acc: 0.8712 - val_loss: 0.8047 - val_acc: 0.7256\n",
      "Epoch 378/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3321 - acc: 0.9100 - val_loss: 0.8407 - val_acc: 0.7744\n",
      "Epoch 379/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3359 - acc: 0.9059 - val_loss: 0.8013 - val_acc: 0.7561\n",
      "Epoch 380/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3739 - acc: 0.8793 - val_loss: 0.7137 - val_acc: 0.8110\n",
      "Epoch 381/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3557 - acc: 0.9141 - val_loss: 0.8528 - val_acc: 0.7134\n",
      "Epoch 382/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3984 - acc: 0.8916 - val_loss: 0.8559 - val_acc: 0.6768\n",
      "Epoch 383/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3609 - acc: 0.8998 - val_loss: 0.7625 - val_acc: 0.7805\n",
      "Epoch 384/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3650 - acc: 0.9039 - val_loss: 0.7753 - val_acc: 0.7927\n",
      "Epoch 385/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3421 - acc: 0.9141 - val_loss: 0.8175 - val_acc: 0.7378\n",
      "Epoch 386/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3976 - acc: 0.8875 - val_loss: 1.0741 - val_acc: 0.6768\n",
      "Epoch 387/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4243 - acc: 0.8671 - val_loss: 0.7390 - val_acc: 0.7561\n",
      "Epoch 388/1000\n",
      "489/489 [==============================] - ETA: 0s - loss: 0.4037 - acc: 0.937 - 0s 131us/step - loss: 0.5159 - acc: 0.8323 - val_loss: 0.7428 - val_acc: 0.7805\n",
      "Epoch 389/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4379 - acc: 0.8691 - val_loss: 0.7434 - val_acc: 0.7439\n",
      "Epoch 390/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.4216 - acc: 0.8814 - val_loss: 0.7809 - val_acc: 0.7195\n",
      "Epoch 391/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4194 - acc: 0.8732 - val_loss: 0.8156 - val_acc: 0.7256\n",
      "Epoch 392/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3983 - acc: 0.8875 - val_loss: 0.8044 - val_acc: 0.7012\n",
      "Epoch 393/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3927 - acc: 0.8916 - val_loss: 0.7920 - val_acc: 0.7439\n",
      "Epoch 394/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4006 - acc: 0.8773 - val_loss: 0.7843 - val_acc: 0.7500\n",
      "Epoch 395/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4035 - acc: 0.8732 - val_loss: 0.7848 - val_acc: 0.7744\n",
      "Epoch 396/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3841 - acc: 0.8896 - val_loss: 0.7796 - val_acc: 0.7500\n",
      "Epoch 397/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3809 - acc: 0.8875 - val_loss: 0.9546 - val_acc: 0.7012\n",
      "Epoch 398/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3621 - acc: 0.9018 - val_loss: 0.9935 - val_acc: 0.6402\n",
      "Epoch 399/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.4829 - acc: 0.8200 - val_loss: 0.8416 - val_acc: 0.6951\n",
      "Epoch 400/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4300 - acc: 0.8589 - val_loss: 0.8007 - val_acc: 0.7500\n",
      "Epoch 401/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4021 - acc: 0.8712 - val_loss: 0.8043 - val_acc: 0.7683\n",
      "Epoch 402/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4474 - acc: 0.8364 - val_loss: 0.8270 - val_acc: 0.7439\n",
      "Epoch 403/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.4548 - acc: 0.8609 - val_loss: 0.8014 - val_acc: 0.7256\n",
      "Epoch 404/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.4340 - acc: 0.8691 - val_loss: 0.8078 - val_acc: 0.7378\n",
      "Epoch 405/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.4552 - acc: 0.8548 - val_loss: 0.8194 - val_acc: 0.7561\n",
      "Epoch 406/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.5279 - acc: 0.8098 - val_loss: 0.8723 - val_acc: 0.7805\n",
      "Epoch 407/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4470 - acc: 0.8630 - val_loss: 0.8461 - val_acc: 0.7317\n",
      "Epoch 408/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4453 - acc: 0.8569 - val_loss: 0.8259 - val_acc: 0.7500\n",
      "Epoch 409/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.4339 - acc: 0.8650 - val_loss: 0.8590 - val_acc: 0.7012\n",
      "Epoch 410/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.4344 - acc: 0.8712 - val_loss: 0.9166 - val_acc: 0.6951\n",
      "Epoch 411/1000\n",
      "489/489 [==============================] - 0s 186us/step - loss: 0.4158 - acc: 0.8732 - val_loss: 0.9403 - val_acc: 0.6646\n",
      "Epoch 412/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.4147 - acc: 0.8793 - val_loss: 0.8065 - val_acc: 0.7317\n",
      "Epoch 413/1000\n",
      "489/489 [==============================] - 0s 182us/step - loss: 0.4051 - acc: 0.8773 - val_loss: 0.8229 - val_acc: 0.7317\n",
      "Epoch 414/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3939 - acc: 0.8896 - val_loss: 0.8379 - val_acc: 0.7195\n",
      "Epoch 415/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 147us/step - loss: 0.4109 - acc: 0.8793 - val_loss: 0.8233 - val_acc: 0.7317\n",
      "Epoch 416/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4054 - acc: 0.8793 - val_loss: 0.8742 - val_acc: 0.7073\n",
      "Epoch 417/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3957 - acc: 0.8875 - val_loss: 0.8593 - val_acc: 0.7073\n",
      "Epoch 418/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.3883 - acc: 0.8875 - val_loss: 0.8953 - val_acc: 0.7256\n",
      "Epoch 419/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4018 - acc: 0.8896 - val_loss: 0.9397 - val_acc: 0.6890\n",
      "Epoch 420/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3863 - acc: 0.8793 - val_loss: 0.8051 - val_acc: 0.7500\n",
      "Epoch 421/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3925 - acc: 0.8814 - val_loss: 0.9138 - val_acc: 0.7073\n",
      "Epoch 422/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4114 - acc: 0.8691 - val_loss: 0.8085 - val_acc: 0.7500\n",
      "Epoch 423/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4059 - acc: 0.8753 - val_loss: 0.7975 - val_acc: 0.7561\n",
      "Epoch 424/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3866 - acc: 0.8732 - val_loss: 0.8708 - val_acc: 0.7073\n",
      "Epoch 425/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3720 - acc: 0.8834 - val_loss: 0.9891 - val_acc: 0.6646\n",
      "Epoch 426/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3986 - acc: 0.8691 - val_loss: 1.0439 - val_acc: 0.6646\n",
      "Epoch 427/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3860 - acc: 0.8712 - val_loss: 0.8334 - val_acc: 0.7256\n",
      "Epoch 428/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.3785 - acc: 0.8916 - val_loss: 1.0016 - val_acc: 0.6646\n",
      "Epoch 429/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3552 - acc: 0.8875 - val_loss: 0.8712 - val_acc: 0.7317\n",
      "Epoch 430/1000\n",
      "489/489 [==============================] - ETA: 0s - loss: 0.3576 - acc: 0.906 - 0s 133us/step - loss: 0.3571 - acc: 0.8978 - val_loss: 0.7712 - val_acc: 0.7805\n",
      "Epoch 431/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3380 - acc: 0.9059 - val_loss: 0.8867 - val_acc: 0.7073\n",
      "Epoch 432/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3337 - acc: 0.9018 - val_loss: 0.7925 - val_acc: 0.7500\n",
      "Epoch 433/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3506 - acc: 0.8978 - val_loss: 0.8686 - val_acc: 0.7317\n",
      "Epoch 434/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.3491 - acc: 0.8957 - val_loss: 0.8255 - val_acc: 0.7378\n",
      "Epoch 435/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3540 - acc: 0.8937 - val_loss: 0.8828 - val_acc: 0.7378\n",
      "Epoch 436/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3609 - acc: 0.8834 - val_loss: 1.0291 - val_acc: 0.6463\n",
      "Epoch 437/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3951 - acc: 0.8732 - val_loss: 0.8025 - val_acc: 0.7622\n",
      "Epoch 438/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3835 - acc: 0.8896 - val_loss: 0.7816 - val_acc: 0.7866\n",
      "Epoch 439/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3605 - acc: 0.9018 - val_loss: 0.9031 - val_acc: 0.7378\n",
      "Epoch 440/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3610 - acc: 0.8916 - val_loss: 0.8119 - val_acc: 0.7683\n",
      "Epoch 441/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3772 - acc: 0.8753 - val_loss: 0.8807 - val_acc: 0.7439\n",
      "Epoch 442/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3782 - acc: 0.8937 - val_loss: 0.7631 - val_acc: 0.7927\n",
      "Epoch 443/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3538 - acc: 0.8916 - val_loss: 0.7654 - val_acc: 0.7866\n",
      "Epoch 444/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3333 - acc: 0.8937 - val_loss: 0.9151 - val_acc: 0.7317\n",
      "Epoch 445/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3436 - acc: 0.8896 - val_loss: 0.7631 - val_acc: 0.7988\n",
      "Epoch 446/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3314 - acc: 0.9039 - val_loss: 0.9821 - val_acc: 0.7256\n",
      "Epoch 447/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3867 - acc: 0.8814 - val_loss: 0.8289 - val_acc: 0.7500\n",
      "Epoch 448/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3395 - acc: 0.8957 - val_loss: 0.8046 - val_acc: 0.7500\n",
      "Epoch 449/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.3171 - acc: 0.9059 - val_loss: 0.8408 - val_acc: 0.7561\n",
      "Epoch 450/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.4079 - acc: 0.8589 - val_loss: 0.8219 - val_acc: 0.7744\n",
      "Epoch 451/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3750 - acc: 0.8957 - val_loss: 0.9192 - val_acc: 0.7073\n",
      "Epoch 452/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3425 - acc: 0.8998 - val_loss: 1.0060 - val_acc: 0.7134\n",
      "Epoch 453/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3153 - acc: 0.9039 - val_loss: 0.8856 - val_acc: 0.7561\n",
      "Epoch 454/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3678 - acc: 0.8937 - val_loss: 0.8315 - val_acc: 0.7378\n",
      "Epoch 455/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3667 - acc: 0.8978 - val_loss: 0.7863 - val_acc: 0.7927\n",
      "Epoch 456/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3393 - acc: 0.9059 - val_loss: 0.7948 - val_acc: 0.7683\n",
      "Epoch 457/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3372 - acc: 0.8957 - val_loss: 0.8222 - val_acc: 0.7561\n",
      "Epoch 458/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3145 - acc: 0.9121 - val_loss: 0.8383 - val_acc: 0.7744\n",
      "Epoch 459/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3379 - acc: 0.9080 - val_loss: 0.8385 - val_acc: 0.7805\n",
      "Epoch 460/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3960 - acc: 0.8773 - val_loss: 0.8741 - val_acc: 0.6829\n",
      "Epoch 461/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3873 - acc: 0.8916 - val_loss: 0.8109 - val_acc: 0.7500\n",
      "Epoch 462/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3685 - acc: 0.8834 - val_loss: 0.9826 - val_acc: 0.6585\n",
      "Epoch 463/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3589 - acc: 0.9059 - val_loss: 0.8731 - val_acc: 0.7500\n",
      "Epoch 464/1000\n",
      "489/489 [==============================] - 0s 177us/step - loss: 0.3551 - acc: 0.8916 - val_loss: 0.8902 - val_acc: 0.7073\n",
      "Epoch 465/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.3477 - acc: 0.8937 - val_loss: 1.0139 - val_acc: 0.6646\n",
      "Epoch 466/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3675 - acc: 0.8896 - val_loss: 0.8788 - val_acc: 0.7561\n",
      "Epoch 467/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3617 - acc: 0.8916 - val_loss: 0.7969 - val_acc: 0.7866\n",
      "Epoch 468/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3722 - acc: 0.8793 - val_loss: 0.8965 - val_acc: 0.7256\n",
      "Epoch 469/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3486 - acc: 0.8916 - val_loss: 0.8496 - val_acc: 0.7439\n",
      "Epoch 470/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3288 - acc: 0.9080 - val_loss: 0.8538 - val_acc: 0.7744\n",
      "Epoch 471/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3356 - acc: 0.9100 - val_loss: 0.8365 - val_acc: 0.7561\n",
      "Epoch 472/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3534 - acc: 0.8937 - val_loss: 0.8676 - val_acc: 0.7988\n",
      "Epoch 473/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.4530 - acc: 0.8896 - val_loss: 0.8192 - val_acc: 0.7988\n",
      "Epoch 474/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 147us/step - loss: 0.3827 - acc: 0.8875 - val_loss: 0.9021 - val_acc: 0.6829\n",
      "Epoch 475/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3824 - acc: 0.8855 - val_loss: 0.9423 - val_acc: 0.7134\n",
      "Epoch 476/1000\n",
      "489/489 [==============================] - 0s 122us/step - loss: 0.3668 - acc: 0.8855 - val_loss: 0.8142 - val_acc: 0.7378\n",
      "Epoch 477/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3665 - acc: 0.9018 - val_loss: 0.7799 - val_acc: 0.7744\n",
      "Epoch 478/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4007 - acc: 0.8671 - val_loss: 0.8385 - val_acc: 0.7317\n",
      "Epoch 479/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.3934 - acc: 0.8671 - val_loss: 0.8521 - val_acc: 0.7439\n",
      "Epoch 480/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.3743 - acc: 0.8855 - val_loss: 0.8496 - val_acc: 0.7317\n",
      "Epoch 481/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3306 - acc: 0.9039 - val_loss: 0.8617 - val_acc: 0.7561\n",
      "Epoch 482/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3021 - acc: 0.9162 - val_loss: 0.9526 - val_acc: 0.7195\n",
      "Epoch 483/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3122 - acc: 0.9080 - val_loss: 0.9966 - val_acc: 0.7195\n",
      "Epoch 484/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2889 - acc: 0.9202 - val_loss: 0.9554 - val_acc: 0.7256\n",
      "Epoch 485/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3003 - acc: 0.9223 - val_loss: 0.7813 - val_acc: 0.7866\n",
      "Epoch 486/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3042 - acc: 0.9182 - val_loss: 0.8877 - val_acc: 0.7500\n",
      "Epoch 487/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3393 - acc: 0.8957 - val_loss: 0.8676 - val_acc: 0.7561\n",
      "Epoch 488/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3820 - acc: 0.8793 - val_loss: 0.8620 - val_acc: 0.7683\n",
      "Epoch 489/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3490 - acc: 0.8875 - val_loss: 0.8333 - val_acc: 0.7439\n",
      "Epoch 490/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3283 - acc: 0.9080 - val_loss: 0.9022 - val_acc: 0.7500\n",
      "Epoch 491/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3289 - acc: 0.9039 - val_loss: 0.9081 - val_acc: 0.7439\n",
      "Epoch 492/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3191 - acc: 0.8998 - val_loss: 0.9159 - val_acc: 0.7561\n",
      "Epoch 493/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3699 - acc: 0.8875 - val_loss: 0.8158 - val_acc: 0.7805\n",
      "Epoch 494/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3790 - acc: 0.8855 - val_loss: 0.8581 - val_acc: 0.7500\n",
      "Epoch 495/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.3719 - acc: 0.8855 - val_loss: 0.8155 - val_acc: 0.7622\n",
      "Epoch 496/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3836 - acc: 0.8814 - val_loss: 0.9721 - val_acc: 0.7317\n",
      "Epoch 497/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3514 - acc: 0.8875 - val_loss: 0.8174 - val_acc: 0.7622\n",
      "Epoch 498/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3325 - acc: 0.9121 - val_loss: 0.9397 - val_acc: 0.7317\n",
      "Epoch 499/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3380 - acc: 0.9059 - val_loss: 0.7939 - val_acc: 0.7744\n",
      "Epoch 500/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3125 - acc: 0.9080 - val_loss: 1.0888 - val_acc: 0.7073\n",
      "Epoch 501/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.4090 - acc: 0.8978 - val_loss: 0.8612 - val_acc: 0.7561\n",
      "Epoch 502/1000\n",
      "489/489 [==============================] - ETA: 0s - loss: 0.3522 - acc: 0.897 - 0s 143us/step - loss: 0.3543 - acc: 0.8957 - val_loss: 0.8421 - val_acc: 0.7256\n",
      "Epoch 503/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3499 - acc: 0.8937 - val_loss: 0.8926 - val_acc: 0.7256\n",
      "Epoch 504/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3405 - acc: 0.9039 - val_loss: 0.8457 - val_acc: 0.7500\n",
      "Epoch 505/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3532 - acc: 0.8814 - val_loss: 0.8525 - val_acc: 0.7439\n",
      "Epoch 506/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3682 - acc: 0.8957 - val_loss: 0.8552 - val_acc: 0.7500\n",
      "Epoch 507/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3206 - acc: 0.9080 - val_loss: 0.7969 - val_acc: 0.7561\n",
      "Epoch 508/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3126 - acc: 0.9223 - val_loss: 1.0100 - val_acc: 0.7012\n",
      "Epoch 509/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3516 - acc: 0.8875 - val_loss: 0.8606 - val_acc: 0.7561\n",
      "Epoch 510/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.3054 - acc: 0.9243 - val_loss: 0.8788 - val_acc: 0.7439\n",
      "Epoch 511/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.2944 - acc: 0.9182 - val_loss: 0.8798 - val_acc: 0.7683\n",
      "Epoch 512/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3555 - acc: 0.9080 - val_loss: 0.8411 - val_acc: 0.8049\n",
      "Epoch 513/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3112 - acc: 0.9141 - val_loss: 0.8212 - val_acc: 0.7561\n",
      "Epoch 514/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3292 - acc: 0.9039 - val_loss: 0.8667 - val_acc: 0.7378\n",
      "Epoch 515/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3191 - acc: 0.9080 - val_loss: 0.8256 - val_acc: 0.7866\n",
      "Epoch 516/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3054 - acc: 0.9141 - val_loss: 0.8159 - val_acc: 0.7683\n",
      "Epoch 517/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.2847 - acc: 0.9182 - val_loss: 0.9539 - val_acc: 0.7561\n",
      "Epoch 518/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3488 - acc: 0.8916 - val_loss: 0.7703 - val_acc: 0.7927\n",
      "Epoch 519/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3358 - acc: 0.8957 - val_loss: 0.7886 - val_acc: 0.8110\n",
      "Epoch 520/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3526 - acc: 0.8916 - val_loss: 0.9444 - val_acc: 0.6829\n",
      "Epoch 521/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3433 - acc: 0.8998 - val_loss: 1.0731 - val_acc: 0.7012\n",
      "Epoch 522/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3400 - acc: 0.8834 - val_loss: 0.8461 - val_acc: 0.7744\n",
      "Epoch 523/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.3581 - acc: 0.8773 - val_loss: 0.9277 - val_acc: 0.6890\n",
      "Epoch 524/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3338 - acc: 0.8834 - val_loss: 1.0029 - val_acc: 0.7195\n",
      "Epoch 525/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.2957 - acc: 0.9243 - val_loss: 0.9510 - val_acc: 0.7561\n",
      "Epoch 526/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.3789 - acc: 0.8630 - val_loss: 0.9105 - val_acc: 0.7622\n",
      "Epoch 527/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3312 - acc: 0.9080 - val_loss: 0.8096 - val_acc: 0.7744\n",
      "Epoch 528/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2708 - acc: 0.9366 - val_loss: 0.8312 - val_acc: 0.7622\n",
      "Epoch 529/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3573 - acc: 0.9059 - val_loss: 0.8249 - val_acc: 0.7622\n",
      "Epoch 530/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3205 - acc: 0.9039 - val_loss: 0.8608 - val_acc: 0.7317\n",
      "Epoch 531/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3327 - acc: 0.9100 - val_loss: 1.0612 - val_acc: 0.6890\n",
      "Epoch 532/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3181 - acc: 0.9121 - val_loss: 0.8892 - val_acc: 0.7561\n",
      "Epoch 533/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 147us/step - loss: 0.3560 - acc: 0.8978 - val_loss: 0.9208 - val_acc: 0.7866\n",
      "Epoch 534/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3701 - acc: 0.8998 - val_loss: 0.7910 - val_acc: 0.7561\n",
      "Epoch 535/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3465 - acc: 0.8937 - val_loss: 0.7258 - val_acc: 0.7927\n",
      "Epoch 536/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3501 - acc: 0.8978 - val_loss: 0.7417 - val_acc: 0.7744\n",
      "Epoch 537/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3167 - acc: 0.9018 - val_loss: 0.8934 - val_acc: 0.7256\n",
      "Epoch 538/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2854 - acc: 0.9223 - val_loss: 0.9262 - val_acc: 0.7561\n",
      "Epoch 539/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4756 - acc: 0.8630 - val_loss: 0.8177 - val_acc: 0.7378\n",
      "Epoch 540/1000\n",
      "489/489 [==============================] - 0s 182us/step - loss: 0.3705 - acc: 0.8773 - val_loss: 0.8379 - val_acc: 0.7622\n",
      "Epoch 541/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3515 - acc: 0.8937 - val_loss: 0.8353 - val_acc: 0.7317\n",
      "Epoch 542/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3430 - acc: 0.8998 - val_loss: 0.8106 - val_acc: 0.7561\n",
      "Epoch 543/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.3557 - acc: 0.8916 - val_loss: 0.8103 - val_acc: 0.7622\n",
      "Epoch 544/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3441 - acc: 0.8896 - val_loss: 0.8487 - val_acc: 0.7439\n",
      "Epoch 545/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.3417 - acc: 0.8998 - val_loss: 0.8542 - val_acc: 0.7317\n",
      "Epoch 546/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3296 - acc: 0.8978 - val_loss: 0.9016 - val_acc: 0.7256\n",
      "Epoch 547/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3488 - acc: 0.8896 - val_loss: 0.8114 - val_acc: 0.7561\n",
      "Epoch 548/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3246 - acc: 0.9039 - val_loss: 0.9407 - val_acc: 0.7378\n",
      "Epoch 549/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3175 - acc: 0.8998 - val_loss: 0.8255 - val_acc: 0.7683\n",
      "Epoch 550/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3069 - acc: 0.9080 - val_loss: 0.9089 - val_acc: 0.7561\n",
      "Epoch 551/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3768 - acc: 0.9018 - val_loss: 0.8931 - val_acc: 0.7134\n",
      "Epoch 552/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3406 - acc: 0.9018 - val_loss: 0.8743 - val_acc: 0.7317\n",
      "Epoch 553/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3158 - acc: 0.9121 - val_loss: 0.9681 - val_acc: 0.6585\n",
      "Epoch 554/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3384 - acc: 0.8814 - val_loss: 0.7963 - val_acc: 0.7683\n",
      "Epoch 555/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.2750 - acc: 0.9243 - val_loss: 0.8518 - val_acc: 0.7378\n",
      "Epoch 556/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3262 - acc: 0.9100 - val_loss: 0.8502 - val_acc: 0.7805\n",
      "Epoch 557/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2873 - acc: 0.9100 - val_loss: 0.8126 - val_acc: 0.7561\n",
      "Epoch 558/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3485 - acc: 0.8814 - val_loss: 0.7522 - val_acc: 0.7927\n",
      "Epoch 559/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3320 - acc: 0.8998 - val_loss: 0.7716 - val_acc: 0.7805\n",
      "Epoch 560/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.3153 - acc: 0.9059 - val_loss: 0.8221 - val_acc: 0.7500\n",
      "Epoch 561/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2864 - acc: 0.9162 - val_loss: 0.9336 - val_acc: 0.7439\n",
      "Epoch 562/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2860 - acc: 0.9141 - val_loss: 0.7981 - val_acc: 0.8049\n",
      "Epoch 563/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.4161 - acc: 0.8712 - val_loss: 0.9691 - val_acc: 0.6890\n",
      "Epoch 564/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.3696 - acc: 0.8773 - val_loss: 0.8751 - val_acc: 0.7317\n",
      "Epoch 565/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3951 - acc: 0.8630 - val_loss: 0.8349 - val_acc: 0.7256\n",
      "Epoch 566/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3285 - acc: 0.9080 - val_loss: 0.7673 - val_acc: 0.7805\n",
      "Epoch 567/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3188 - acc: 0.9080 - val_loss: 0.8956 - val_acc: 0.7134\n",
      "Epoch 568/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3116 - acc: 0.9080 - val_loss: 0.8704 - val_acc: 0.7439\n",
      "Epoch 569/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2880 - acc: 0.9141 - val_loss: 0.8828 - val_acc: 0.7500\n",
      "Epoch 570/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.2742 - acc: 0.9305 - val_loss: 0.9285 - val_acc: 0.7439\n",
      "Epoch 571/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3223 - acc: 0.9039 - val_loss: 0.9738 - val_acc: 0.6768\n",
      "Epoch 572/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3412 - acc: 0.8896 - val_loss: 0.7658 - val_acc: 0.8171\n",
      "Epoch 573/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3327 - acc: 0.8998 - val_loss: 0.8384 - val_acc: 0.7622\n",
      "Epoch 574/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3550 - acc: 0.8896 - val_loss: 0.8470 - val_acc: 0.7622\n",
      "Epoch 575/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3151 - acc: 0.9018 - val_loss: 0.9605 - val_acc: 0.7378\n",
      "Epoch 576/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3355 - acc: 0.9080 - val_loss: 1.0243 - val_acc: 0.7073\n",
      "Epoch 577/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.7652 - acc: 0.8446 - val_loss: 1.0644 - val_acc: 0.7134\n",
      "Epoch 578/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.7317 - acc: 0.8139 - val_loss: 0.8063 - val_acc: 0.7622\n",
      "Epoch 579/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3812 - acc: 0.8773 - val_loss: 0.8222 - val_acc: 0.7378\n",
      "Epoch 580/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3675 - acc: 0.8793 - val_loss: 0.9167 - val_acc: 0.7195\n",
      "Epoch 581/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3544 - acc: 0.8916 - val_loss: 0.8537 - val_acc: 0.7378\n",
      "Epoch 582/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3439 - acc: 0.8957 - val_loss: 0.8614 - val_acc: 0.7256\n",
      "Epoch 583/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3737 - acc: 0.8937 - val_loss: 0.9174 - val_acc: 0.7134\n",
      "Epoch 584/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3409 - acc: 0.9018 - val_loss: 0.9135 - val_acc: 0.7500\n",
      "Epoch 585/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.3260 - acc: 0.9039 - val_loss: 0.8304 - val_acc: 0.7683\n",
      "Epoch 586/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.3230 - acc: 0.9059 - val_loss: 0.8552 - val_acc: 0.7988\n",
      "Epoch 587/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3014 - acc: 0.9182 - val_loss: 0.8749 - val_acc: 0.7622\n",
      "Epoch 588/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3401 - acc: 0.8916 - val_loss: 0.7846 - val_acc: 0.8110\n",
      "Epoch 589/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.3633 - acc: 0.8793 - val_loss: 0.8174 - val_acc: 0.7683\n",
      "Epoch 590/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2886 - acc: 0.9202 - val_loss: 0.8092 - val_acc: 0.7561\n",
      "Epoch 591/1000\n",
      "489/489 [==============================] - 0s 179us/step - loss: 0.4267 - acc: 0.8712 - val_loss: 0.8112 - val_acc: 0.8110\n",
      "Epoch 592/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 139us/step - loss: 0.3516 - acc: 0.8957 - val_loss: 0.7907 - val_acc: 0.7683\n",
      "Epoch 593/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3146 - acc: 0.9080 - val_loss: 0.9692 - val_acc: 0.6890\n",
      "Epoch 594/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3297 - acc: 0.9059 - val_loss: 0.8497 - val_acc: 0.7500\n",
      "Epoch 595/1000\n",
      "489/489 [==============================] - 0s 124us/step - loss: 0.3024 - acc: 0.9018 - val_loss: 0.8420 - val_acc: 0.7683\n",
      "Epoch 596/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2933 - acc: 0.9162 - val_loss: 0.8710 - val_acc: 0.7378\n",
      "Epoch 597/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2953 - acc: 0.9141 - val_loss: 0.8149 - val_acc: 0.7927\n",
      "Epoch 598/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2809 - acc: 0.9243 - val_loss: 0.8620 - val_acc: 0.7683\n",
      "Epoch 599/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2681 - acc: 0.9305 - val_loss: 0.8539 - val_acc: 0.7805\n",
      "Epoch 600/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2880 - acc: 0.9325 - val_loss: 1.0300 - val_acc: 0.6768\n",
      "Epoch 601/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2995 - acc: 0.9080 - val_loss: 0.8476 - val_acc: 0.7866\n",
      "Epoch 602/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.2782 - acc: 0.9202 - val_loss: 0.8627 - val_acc: 0.7683\n",
      "Epoch 603/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2878 - acc: 0.9162 - val_loss: 0.8912 - val_acc: 0.7500\n",
      "Epoch 604/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2822 - acc: 0.9202 - val_loss: 0.9340 - val_acc: 0.7317\n",
      "Epoch 605/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.2722 - acc: 0.9243 - val_loss: 0.8639 - val_acc: 0.7927\n",
      "Epoch 606/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2673 - acc: 0.9223 - val_loss: 0.9018 - val_acc: 0.7683\n",
      "Epoch 607/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2766 - acc: 0.9305 - val_loss: 0.8024 - val_acc: 0.8110\n",
      "Epoch 608/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2675 - acc: 0.9202 - val_loss: 0.9168 - val_acc: 0.7622\n",
      "Epoch 609/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3978 - acc: 0.9080 - val_loss: 0.8988 - val_acc: 0.7378\n",
      "Epoch 610/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3740 - acc: 0.8916 - val_loss: 0.7953 - val_acc: 0.7927\n",
      "Epoch 611/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2821 - acc: 0.9141 - val_loss: 0.7683 - val_acc: 0.8049\n",
      "Epoch 612/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2869 - acc: 0.9223 - val_loss: 0.8912 - val_acc: 0.7195\n",
      "Epoch 613/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2708 - acc: 0.9182 - val_loss: 0.8293 - val_acc: 0.7439\n",
      "Epoch 614/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.2629 - acc: 0.9346 - val_loss: 0.8383 - val_acc: 0.7744\n",
      "Epoch 615/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3021 - acc: 0.9182 - val_loss: 0.8734 - val_acc: 0.7622\n",
      "Epoch 616/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2729 - acc: 0.9284 - val_loss: 0.8736 - val_acc: 0.7561\n",
      "Epoch 617/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2671 - acc: 0.9223 - val_loss: 0.8090 - val_acc: 0.7927\n",
      "Epoch 618/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2979 - acc: 0.9121 - val_loss: 0.8455 - val_acc: 0.7866\n",
      "Epoch 619/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.3888 - acc: 0.8630 - val_loss: 0.9770 - val_acc: 0.7073\n",
      "Epoch 620/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3456 - acc: 0.8793 - val_loss: 0.9278 - val_acc: 0.7500\n",
      "Epoch 621/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3300 - acc: 0.8978 - val_loss: 0.9124 - val_acc: 0.7256\n",
      "Epoch 622/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3266 - acc: 0.9039 - val_loss: 0.8351 - val_acc: 0.7744\n",
      "Epoch 623/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2990 - acc: 0.9121 - val_loss: 0.9952 - val_acc: 0.7073\n",
      "Epoch 624/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2795 - acc: 0.9182 - val_loss: 0.9207 - val_acc: 0.7744\n",
      "Epoch 625/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2819 - acc: 0.9223 - val_loss: 0.8756 - val_acc: 0.7683\n",
      "Epoch 626/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3773 - acc: 0.8671 - val_loss: 0.9184 - val_acc: 0.7439\n",
      "Epoch 627/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3172 - acc: 0.8957 - val_loss: 0.8440 - val_acc: 0.7927\n",
      "Epoch 628/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2950 - acc: 0.9121 - val_loss: 0.8739 - val_acc: 0.7683\n",
      "Epoch 629/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2596 - acc: 0.9284 - val_loss: 0.8501 - val_acc: 0.7622\n",
      "Epoch 630/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2496 - acc: 0.9387 - val_loss: 0.8419 - val_acc: 0.7866\n",
      "Epoch 631/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2624 - acc: 0.9223 - val_loss: 0.9639 - val_acc: 0.7378\n",
      "Epoch 632/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.4974 - acc: 0.8834 - val_loss: 0.8969 - val_acc: 0.7622\n",
      "Epoch 633/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3806 - acc: 0.8732 - val_loss: 0.8535 - val_acc: 0.7134\n",
      "Epoch 634/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3657 - acc: 0.8773 - val_loss: 0.8455 - val_acc: 0.7317\n",
      "Epoch 635/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3334 - acc: 0.8937 - val_loss: 0.9888 - val_acc: 0.6524\n",
      "Epoch 636/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3360 - acc: 0.8896 - val_loss: 0.9284 - val_acc: 0.7134\n",
      "Epoch 637/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3210 - acc: 0.9080 - val_loss: 0.8659 - val_acc: 0.7439\n",
      "Epoch 638/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3088 - acc: 0.9141 - val_loss: 0.7920 - val_acc: 0.7988\n",
      "Epoch 639/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2945 - acc: 0.9141 - val_loss: 0.8366 - val_acc: 0.7561\n",
      "Epoch 640/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2733 - acc: 0.9264 - val_loss: 0.8787 - val_acc: 0.7683\n",
      "Epoch 641/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3230 - acc: 0.9305 - val_loss: 0.8849 - val_acc: 0.7561\n",
      "Epoch 642/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.2799 - acc: 0.9264 - val_loss: 0.8693 - val_acc: 0.7439\n",
      "Epoch 643/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2743 - acc: 0.9141 - val_loss: 0.8893 - val_acc: 0.7561\n",
      "Epoch 644/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.3419 - acc: 0.8937 - val_loss: 0.9772 - val_acc: 0.6768\n",
      "Epoch 645/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3653 - acc: 0.8814 - val_loss: 0.9459 - val_acc: 0.6768\n",
      "Epoch 646/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3438 - acc: 0.8834 - val_loss: 0.8728 - val_acc: 0.7134\n",
      "Epoch 647/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3281 - acc: 0.8916 - val_loss: 0.8578 - val_acc: 0.7317\n",
      "Epoch 648/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3327 - acc: 0.8773 - val_loss: 0.8774 - val_acc: 0.7378\n",
      "Epoch 649/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3101 - acc: 0.8916 - val_loss: 0.8479 - val_acc: 0.7317\n",
      "Epoch 650/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3403 - acc: 0.8712 - val_loss: 0.8484 - val_acc: 0.7622\n",
      "Epoch 651/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 145us/step - loss: 0.3871 - acc: 0.8671 - val_loss: 0.9237 - val_acc: 0.6646\n",
      "Epoch 652/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3419 - acc: 0.8957 - val_loss: 0.9715 - val_acc: 0.7500\n",
      "Epoch 653/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3189 - acc: 0.8937 - val_loss: 0.8292 - val_acc: 0.7500\n",
      "Epoch 654/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3116 - acc: 0.8957 - val_loss: 0.8136 - val_acc: 0.7561\n",
      "Epoch 655/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3493 - acc: 0.8814 - val_loss: 0.9160 - val_acc: 0.7378\n",
      "Epoch 656/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3897 - acc: 0.8834 - val_loss: 0.8324 - val_acc: 0.7378\n",
      "Epoch 657/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3530 - acc: 0.8650 - val_loss: 0.9847 - val_acc: 0.6402\n",
      "Epoch 658/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3257 - acc: 0.8855 - val_loss: 1.0869 - val_acc: 0.6341\n",
      "Epoch 659/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.3660 - acc: 0.8630 - val_loss: 0.7857 - val_acc: 0.7744\n",
      "Epoch 660/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3247 - acc: 0.8875 - val_loss: 0.9406 - val_acc: 0.7012\n",
      "Epoch 661/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3039 - acc: 0.8998 - val_loss: 0.9547 - val_acc: 0.6951\n",
      "Epoch 662/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2885 - acc: 0.8957 - val_loss: 0.8883 - val_acc: 0.7134\n",
      "Epoch 663/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2920 - acc: 0.8937 - val_loss: 0.9569 - val_acc: 0.7256\n",
      "Epoch 664/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3213 - acc: 0.8916 - val_loss: 1.0661 - val_acc: 0.7683\n",
      "Epoch 665/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.4631 - acc: 0.8855 - val_loss: 0.8430 - val_acc: 0.7500\n",
      "Epoch 666/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.3404 - acc: 0.8978 - val_loss: 0.9870 - val_acc: 0.7195\n",
      "Epoch 667/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3175 - acc: 0.9018 - val_loss: 0.8997 - val_acc: 0.7134\n",
      "Epoch 668/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3047 - acc: 0.8998 - val_loss: 0.8812 - val_acc: 0.7622\n",
      "Epoch 669/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3324 - acc: 0.8957 - val_loss: 0.9208 - val_acc: 0.7561\n",
      "Epoch 670/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3819 - acc: 0.8732 - val_loss: 0.8458 - val_acc: 0.7378\n",
      "Epoch 671/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3483 - acc: 0.8937 - val_loss: 0.9371 - val_acc: 0.7378\n",
      "Epoch 672/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3155 - acc: 0.8937 - val_loss: 0.9313 - val_acc: 0.7256\n",
      "Epoch 673/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2987 - acc: 0.8978 - val_loss: 0.9225 - val_acc: 0.7683\n",
      "Epoch 674/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2870 - acc: 0.8998 - val_loss: 0.8938 - val_acc: 0.7378\n",
      "Epoch 675/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2771 - acc: 0.9080 - val_loss: 0.8881 - val_acc: 0.7439\n",
      "Epoch 676/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2811 - acc: 0.9100 - val_loss: 0.9380 - val_acc: 0.7439\n",
      "Epoch 677/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2744 - acc: 0.9121 - val_loss: 0.9159 - val_acc: 0.7317\n",
      "Epoch 678/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2975 - acc: 0.8957 - val_loss: 0.9587 - val_acc: 0.7256\n",
      "Epoch 679/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2777 - acc: 0.9202 - val_loss: 0.9601 - val_acc: 0.7256\n",
      "Epoch 680/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2899 - acc: 0.9100 - val_loss: 0.8191 - val_acc: 0.7683\n",
      "Epoch 681/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2821 - acc: 0.9080 - val_loss: 0.8922 - val_acc: 0.7622\n",
      "Epoch 682/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2610 - acc: 0.9100 - val_loss: 0.9610 - val_acc: 0.7317\n",
      "Epoch 683/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.2767 - acc: 0.9018 - val_loss: 0.9122 - val_acc: 0.7744\n",
      "Epoch 684/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3020 - acc: 0.9080 - val_loss: 0.8624 - val_acc: 0.7683\n",
      "Epoch 685/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2775 - acc: 0.9162 - val_loss: 1.0390 - val_acc: 0.7256\n",
      "Epoch 686/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2589 - acc: 0.9182 - val_loss: 0.9503 - val_acc: 0.7500\n",
      "Epoch 687/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3607 - acc: 0.8937 - val_loss: 1.3461 - val_acc: 0.6280\n",
      "Epoch 688/1000\n",
      "489/489 [==============================] - 0s 179us/step - loss: 0.4259 - acc: 0.8384 - val_loss: 0.8961 - val_acc: 0.7927\n",
      "Epoch 689/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3719 - acc: 0.8691 - val_loss: 0.8451 - val_acc: 0.6463\n",
      "Epoch 690/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3370 - acc: 0.8793 - val_loss: 0.9288 - val_acc: 0.7073\n",
      "Epoch 691/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3229 - acc: 0.8793 - val_loss: 0.8783 - val_acc: 0.7561\n",
      "Epoch 692/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3345 - acc: 0.8753 - val_loss: 0.8539 - val_acc: 0.7683\n",
      "Epoch 693/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3339 - acc: 0.8793 - val_loss: 0.9133 - val_acc: 0.6951\n",
      "Epoch 694/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3091 - acc: 0.8773 - val_loss: 0.8394 - val_acc: 0.7500\n",
      "Epoch 695/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3036 - acc: 0.8834 - val_loss: 0.9995 - val_acc: 0.7317\n",
      "Epoch 696/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2989 - acc: 0.8978 - val_loss: 0.9641 - val_acc: 0.7317\n",
      "Epoch 697/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2793 - acc: 0.9018 - val_loss: 1.2413 - val_acc: 0.6890\n",
      "Epoch 698/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2883 - acc: 0.8937 - val_loss: 1.1046 - val_acc: 0.6890\n",
      "Epoch 699/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3682 - acc: 0.8978 - val_loss: 1.0407 - val_acc: 0.7256\n",
      "Epoch 700/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3310 - acc: 0.9121 - val_loss: 1.0039 - val_acc: 0.7500\n",
      "Epoch 701/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3059 - acc: 0.9223 - val_loss: 0.9564 - val_acc: 0.7012\n",
      "Epoch 702/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3641 - acc: 0.8753 - val_loss: 0.8781 - val_acc: 0.7378\n",
      "Epoch 703/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.2490 - acc: 0.9346 - val_loss: 0.9052 - val_acc: 0.7256\n",
      "Epoch 704/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2997 - acc: 0.9141 - val_loss: 0.8870 - val_acc: 0.7561\n",
      "Epoch 705/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3141 - acc: 0.8875 - val_loss: 0.9047 - val_acc: 0.7439\n",
      "Epoch 706/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2940 - acc: 0.9080 - val_loss: 0.9340 - val_acc: 0.7500\n",
      "Epoch 707/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2788 - acc: 0.9141 - val_loss: 0.9357 - val_acc: 0.7683\n",
      "Epoch 708/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2644 - acc: 0.9325 - val_loss: 1.1599 - val_acc: 0.6707\n",
      "Epoch 709/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3281 - acc: 0.8937 - val_loss: 1.0221 - val_acc: 0.7256\n",
      "Epoch 710/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 137us/step - loss: 0.3772 - acc: 0.8753 - val_loss: 0.7936 - val_acc: 0.7195\n",
      "Epoch 711/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.3506 - acc: 0.8896 - val_loss: 0.7806 - val_acc: 0.7683\n",
      "Epoch 712/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3211 - acc: 0.9080 - val_loss: 0.7317 - val_acc: 0.7988\n",
      "Epoch 713/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2977 - acc: 0.9141 - val_loss: 0.7887 - val_acc: 0.7256\n",
      "Epoch 714/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3023 - acc: 0.9182 - val_loss: 0.7794 - val_acc: 0.8110\n",
      "Epoch 715/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2844 - acc: 0.9202 - val_loss: 0.7491 - val_acc: 0.8049\n",
      "Epoch 716/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.5001 - acc: 0.8753 - val_loss: 0.7682 - val_acc: 0.7805\n",
      "Epoch 717/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3142 - acc: 0.8998 - val_loss: 0.8140 - val_acc: 0.7073\n",
      "Epoch 718/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3265 - acc: 0.8998 - val_loss: 0.8101 - val_acc: 0.7439\n",
      "Epoch 719/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.3030 - acc: 0.8957 - val_loss: 0.8261 - val_acc: 0.7378\n",
      "Epoch 720/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3110 - acc: 0.8978 - val_loss: 0.8004 - val_acc: 0.7866\n",
      "Epoch 721/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.4077 - acc: 0.8650 - val_loss: 1.0598 - val_acc: 0.6951\n",
      "Epoch 722/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3345 - acc: 0.8937 - val_loss: 0.7960 - val_acc: 0.7256\n",
      "Epoch 723/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3263 - acc: 0.8937 - val_loss: 0.7832 - val_acc: 0.7866\n",
      "Epoch 724/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3225 - acc: 0.8916 - val_loss: 0.7773 - val_acc: 0.8049\n",
      "Epoch 725/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3047 - acc: 0.9080 - val_loss: 0.8511 - val_acc: 0.7744\n",
      "Epoch 726/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2785 - acc: 0.9162 - val_loss: 0.8356 - val_acc: 0.7561\n",
      "Epoch 727/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2549 - acc: 0.9264 - val_loss: 0.8692 - val_acc: 0.7622\n",
      "Epoch 728/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2547 - acc: 0.9305 - val_loss: 1.0353 - val_acc: 0.7195\n",
      "Epoch 729/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3203 - acc: 0.9018 - val_loss: 1.0622 - val_acc: 0.6585\n",
      "Epoch 730/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2892 - acc: 0.9182 - val_loss: 0.8375 - val_acc: 0.7805\n",
      "Epoch 731/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2752 - acc: 0.9141 - val_loss: 1.0419 - val_acc: 0.6829\n",
      "Epoch 732/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2816 - acc: 0.9202 - val_loss: 0.8914 - val_acc: 0.7561\n",
      "Epoch 733/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2612 - acc: 0.9182 - val_loss: 0.8609 - val_acc: 0.7439\n",
      "Epoch 734/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2555 - acc: 0.9305 - val_loss: 0.9309 - val_acc: 0.7500\n",
      "Epoch 735/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2575 - acc: 0.9366 - val_loss: 0.9260 - val_acc: 0.7500\n",
      "Epoch 736/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2857 - acc: 0.9182 - val_loss: 1.1583 - val_acc: 0.6829\n",
      "Epoch 737/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3063 - acc: 0.9080 - val_loss: 0.8772 - val_acc: 0.7683\n",
      "Epoch 738/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.2527 - acc: 0.9407 - val_loss: 0.9691 - val_acc: 0.7439\n",
      "Epoch 739/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2368 - acc: 0.9387 - val_loss: 0.9780 - val_acc: 0.7317\n",
      "Epoch 740/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2555 - acc: 0.9243 - val_loss: 0.9180 - val_acc: 0.7561\n",
      "Epoch 741/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3091 - acc: 0.9100 - val_loss: 1.0871 - val_acc: 0.6829\n",
      "Epoch 742/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2962 - acc: 0.9100 - val_loss: 0.8416 - val_acc: 0.7988\n",
      "Epoch 743/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2740 - acc: 0.9059 - val_loss: 0.8077 - val_acc: 0.8110\n",
      "Epoch 744/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.3941 - acc: 0.8671 - val_loss: 0.9486 - val_acc: 0.7378\n",
      "Epoch 745/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3211 - acc: 0.9018 - val_loss: 0.8566 - val_acc: 0.7988\n",
      "Epoch 746/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2995 - acc: 0.9100 - val_loss: 0.8315 - val_acc: 0.7683\n",
      "Epoch 747/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2816 - acc: 0.9141 - val_loss: 0.8774 - val_acc: 0.7500\n",
      "Epoch 748/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2674 - acc: 0.9243 - val_loss: 0.8772 - val_acc: 0.7866\n",
      "Epoch 749/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2373 - acc: 0.9346 - val_loss: 0.9849 - val_acc: 0.7256\n",
      "Epoch 750/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.2470 - acc: 0.9366 - val_loss: 0.8700 - val_acc: 0.7805\n",
      "Epoch 751/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2664 - acc: 0.9202 - val_loss: 0.9271 - val_acc: 0.7500\n",
      "Epoch 752/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2728 - acc: 0.9182 - val_loss: 0.8675 - val_acc: 0.7927\n",
      "Epoch 753/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2824 - acc: 0.9141 - val_loss: 0.8310 - val_acc: 0.7622\n",
      "Epoch 754/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2873 - acc: 0.9100 - val_loss: 0.9571 - val_acc: 0.7500\n",
      "Epoch 755/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2994 - acc: 0.9100 - val_loss: 0.9790 - val_acc: 0.7073\n",
      "Epoch 756/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2740 - acc: 0.9202 - val_loss: 0.8620 - val_acc: 0.7866\n",
      "Epoch 757/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2679 - acc: 0.9264 - val_loss: 0.9616 - val_acc: 0.7805\n",
      "Epoch 758/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2785 - acc: 0.9121 - val_loss: 1.0729 - val_acc: 0.7134\n",
      "Epoch 759/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2996 - acc: 0.9100 - val_loss: 0.9014 - val_acc: 0.7744\n",
      "Epoch 760/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2658 - acc: 0.9264 - val_loss: 0.9585 - val_acc: 0.7500\n",
      "Epoch 761/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2507 - acc: 0.9346 - val_loss: 0.9876 - val_acc: 0.7378\n",
      "Epoch 762/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2567 - acc: 0.9407 - val_loss: 0.9350 - val_acc: 0.7317\n",
      "Epoch 763/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2462 - acc: 0.9325 - val_loss: 0.8883 - val_acc: 0.7805\n",
      "Epoch 764/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2657 - acc: 0.9162 - val_loss: 1.0189 - val_acc: 0.7439\n",
      "Epoch 765/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2826 - acc: 0.9080 - val_loss: 0.9990 - val_acc: 0.7378\n",
      "Epoch 766/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2566 - acc: 0.9284 - val_loss: 1.0427 - val_acc: 0.7073\n",
      "Epoch 767/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2535 - acc: 0.9284 - val_loss: 1.0235 - val_acc: 0.7256\n",
      "Epoch 768/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2540 - acc: 0.9387 - val_loss: 1.0075 - val_acc: 0.7195\n",
      "Epoch 769/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 155us/step - loss: 0.2436 - acc: 0.9325 - val_loss: 0.9132 - val_acc: 0.7927\n",
      "Epoch 770/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.2524 - acc: 0.9448 - val_loss: 0.9341 - val_acc: 0.7805\n",
      "Epoch 771/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2435 - acc: 0.9264 - val_loss: 1.0468 - val_acc: 0.7439\n",
      "Epoch 772/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2933 - acc: 0.9223 - val_loss: 1.0470 - val_acc: 0.7012\n",
      "Epoch 773/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2611 - acc: 0.9305 - val_loss: 1.0095 - val_acc: 0.6951\n",
      "Epoch 774/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2750 - acc: 0.9141 - val_loss: 0.9865 - val_acc: 0.7439\n",
      "Epoch 775/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2758 - acc: 0.9080 - val_loss: 1.0832 - val_acc: 0.6951\n",
      "Epoch 776/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2785 - acc: 0.9223 - val_loss: 0.9345 - val_acc: 0.7561\n",
      "Epoch 777/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.2901 - acc: 0.9059 - val_loss: 1.0031 - val_acc: 0.7317\n",
      "Epoch 778/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2813 - acc: 0.9141 - val_loss: 1.0014 - val_acc: 0.7256\n",
      "Epoch 779/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2596 - acc: 0.9243 - val_loss: 1.0178 - val_acc: 0.7195\n",
      "Epoch 780/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2457 - acc: 0.9284 - val_loss: 0.9091 - val_acc: 0.7744\n",
      "Epoch 781/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2351 - acc: 0.9264 - val_loss: 0.9124 - val_acc: 0.7683\n",
      "Epoch 782/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2338 - acc: 0.9305 - val_loss: 1.0040 - val_acc: 0.7561\n",
      "Epoch 783/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2869 - acc: 0.9305 - val_loss: 1.1163 - val_acc: 0.6768\n",
      "Epoch 784/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2950 - acc: 0.9039 - val_loss: 0.8797 - val_acc: 0.7683\n",
      "Epoch 785/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2583 - acc: 0.9284 - val_loss: 0.9049 - val_acc: 0.7927\n",
      "Epoch 786/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3052 - acc: 0.9264 - val_loss: 1.5058 - val_acc: 0.6463\n",
      "Epoch 787/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.7420 - acc: 0.8630 - val_loss: 1.1381 - val_acc: 0.6524\n",
      "Epoch 788/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4612 - acc: 0.8650 - val_loss: 0.8451 - val_acc: 0.7134\n",
      "Epoch 789/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3886 - acc: 0.8650 - val_loss: 0.9902 - val_acc: 0.6646\n",
      "Epoch 790/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.4592 - acc: 0.8630 - val_loss: 1.0209 - val_acc: 0.6585\n",
      "Epoch 791/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.4439 - acc: 0.8671 - val_loss: 0.8981 - val_acc: 0.6890\n",
      "Epoch 792/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3965 - acc: 0.8793 - val_loss: 0.9034 - val_acc: 0.7012\n",
      "Epoch 793/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3727 - acc: 0.8773 - val_loss: 0.9764 - val_acc: 0.6463\n",
      "Epoch 794/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.5596 - acc: 0.8834 - val_loss: 1.0759 - val_acc: 0.6524\n",
      "Epoch 795/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.4045 - acc: 0.8937 - val_loss: 0.8755 - val_acc: 0.7378\n",
      "Epoch 796/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3452 - acc: 0.8916 - val_loss: 0.8744 - val_acc: 0.7378\n",
      "Epoch 797/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3159 - acc: 0.8978 - val_loss: 0.9067 - val_acc: 0.7073\n",
      "Epoch 798/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.3243 - acc: 0.8916 - val_loss: 0.8505 - val_acc: 0.7622\n",
      "Epoch 799/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3534 - acc: 0.8875 - val_loss: 0.8482 - val_acc: 0.7195\n",
      "Epoch 800/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2852 - acc: 0.9080 - val_loss: 0.8786 - val_acc: 0.7317\n",
      "Epoch 801/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2910 - acc: 0.9121 - val_loss: 0.9278 - val_acc: 0.7500\n",
      "Epoch 802/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2983 - acc: 0.9182 - val_loss: 1.0017 - val_acc: 0.7073\n",
      "Epoch 803/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.3028 - acc: 0.9182 - val_loss: 1.0367 - val_acc: 0.7134\n",
      "Epoch 804/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2616 - acc: 0.9202 - val_loss: 0.8774 - val_acc: 0.7561\n",
      "Epoch 805/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2762 - acc: 0.9202 - val_loss: 0.9154 - val_acc: 0.7500\n",
      "Epoch 806/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2870 - acc: 0.9039 - val_loss: 0.8357 - val_acc: 0.7622\n",
      "Epoch 807/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2870 - acc: 0.9059 - val_loss: 1.0224 - val_acc: 0.7317\n",
      "Epoch 808/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2591 - acc: 0.9223 - val_loss: 0.8955 - val_acc: 0.7683\n",
      "Epoch 809/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2583 - acc: 0.9284 - val_loss: 0.9238 - val_acc: 0.7622\n",
      "Epoch 810/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2466 - acc: 0.9264 - val_loss: 1.0202 - val_acc: 0.7683\n",
      "Epoch 811/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.3591 - acc: 0.8978 - val_loss: 1.0593 - val_acc: 0.6402\n",
      "Epoch 812/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.3468 - acc: 0.8712 - val_loss: 0.9679 - val_acc: 0.6707\n",
      "Epoch 813/1000\n",
      "489/489 [==============================] - 0s 184us/step - loss: 0.3224 - acc: 0.8814 - val_loss: 1.0770 - val_acc: 0.6463\n",
      "Epoch 814/1000\n",
      "489/489 [==============================] - ETA: 0s - loss: 0.3409 - acc: 0.864 - 0s 173us/step - loss: 0.3250 - acc: 0.8753 - val_loss: 0.9118 - val_acc: 0.7134\n",
      "Epoch 815/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2940 - acc: 0.8978 - val_loss: 0.8970 - val_acc: 0.7683\n",
      "Epoch 816/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2499 - acc: 0.9325 - val_loss: 0.9407 - val_acc: 0.7927\n",
      "Epoch 817/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.2910 - acc: 0.9100 - val_loss: 0.9816 - val_acc: 0.6890\n",
      "Epoch 818/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2891 - acc: 0.8957 - val_loss: 1.0254 - val_acc: 0.6951\n",
      "Epoch 819/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2968 - acc: 0.8916 - val_loss: 0.8639 - val_acc: 0.7988\n",
      "Epoch 820/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2676 - acc: 0.9059 - val_loss: 0.9552 - val_acc: 0.7927\n",
      "Epoch 821/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.2765 - acc: 0.9100 - val_loss: 0.9082 - val_acc: 0.7866\n",
      "Epoch 822/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.2807 - acc: 0.9162 - val_loss: 0.9288 - val_acc: 0.7866\n",
      "Epoch 823/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.2586 - acc: 0.9305 - val_loss: 0.9939 - val_acc: 0.7378\n",
      "Epoch 824/1000\n",
      "489/489 [==============================] - 0s 179us/step - loss: 0.2866 - acc: 0.9121 - val_loss: 0.9326 - val_acc: 0.7500\n",
      "Epoch 825/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2457 - acc: 0.9284 - val_loss: 0.9729 - val_acc: 0.7378\n",
      "Epoch 826/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2641 - acc: 0.9223 - val_loss: 0.8644 - val_acc: 0.7683\n",
      "Epoch 827/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2488 - acc: 0.9346 - val_loss: 0.8619 - val_acc: 0.7988\n",
      "Epoch 828/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 151us/step - loss: 0.2415 - acc: 0.9366 - val_loss: 0.9140 - val_acc: 0.7866\n",
      "Epoch 829/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2256 - acc: 0.9346 - val_loss: 0.9070 - val_acc: 0.7744\n",
      "Epoch 830/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2449 - acc: 0.9264 - val_loss: 0.8730 - val_acc: 0.7805\n",
      "Epoch 831/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2638 - acc: 0.9100 - val_loss: 0.9403 - val_acc: 0.7561\n",
      "Epoch 832/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2648 - acc: 0.9243 - val_loss: 0.9185 - val_acc: 0.7988\n",
      "Epoch 833/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.2945 - acc: 0.9100 - val_loss: 1.0830 - val_acc: 0.7317\n",
      "Epoch 834/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.3231 - acc: 0.8896 - val_loss: 0.8571 - val_acc: 0.7866\n",
      "Epoch 835/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2765 - acc: 0.9100 - val_loss: 0.9254 - val_acc: 0.7561\n",
      "Epoch 836/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.2504 - acc: 0.9182 - val_loss: 1.0224 - val_acc: 0.6890\n",
      "Epoch 837/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2507 - acc: 0.9284 - val_loss: 0.9679 - val_acc: 0.7439\n",
      "Epoch 838/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.2272 - acc: 0.9366 - val_loss: 0.9495 - val_acc: 0.7683\n",
      "Epoch 839/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2312 - acc: 0.9346 - val_loss: 0.9294 - val_acc: 0.7866\n",
      "Epoch 840/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2337 - acc: 0.9366 - val_loss: 0.9587 - val_acc: 0.7805\n",
      "Epoch 841/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2586 - acc: 0.9182 - val_loss: 0.9285 - val_acc: 0.7683\n",
      "Epoch 842/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2622 - acc: 0.9223 - val_loss: 1.1130 - val_acc: 0.7012\n",
      "Epoch 843/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2669 - acc: 0.9264 - val_loss: 1.0116 - val_acc: 0.7256\n",
      "Epoch 844/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2307 - acc: 0.9325 - val_loss: 0.9247 - val_acc: 0.7622\n",
      "Epoch 845/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2654 - acc: 0.9346 - val_loss: 0.8831 - val_acc: 0.7927\n",
      "Epoch 846/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3163 - acc: 0.8855 - val_loss: 1.0440 - val_acc: 0.6829\n",
      "Epoch 847/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3092 - acc: 0.8937 - val_loss: 0.8935 - val_acc: 0.7622\n",
      "Epoch 848/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3178 - acc: 0.8773 - val_loss: 0.9366 - val_acc: 0.7256\n",
      "Epoch 849/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2864 - acc: 0.8834 - val_loss: 0.9662 - val_acc: 0.7317\n",
      "Epoch 850/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3000 - acc: 0.9100 - val_loss: 0.9251 - val_acc: 0.7622\n",
      "Epoch 851/1000\n",
      "489/489 [==============================] - 0s 163us/step - loss: 0.2698 - acc: 0.9080 - val_loss: 0.9171 - val_acc: 0.7439\n",
      "Epoch 852/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2721 - acc: 0.9182 - val_loss: 1.2692 - val_acc: 0.7134\n",
      "Epoch 853/1000\n",
      "489/489 [==============================] - 0s 182us/step - loss: 0.3377 - acc: 0.9121 - val_loss: 1.0008 - val_acc: 0.7439\n",
      "Epoch 854/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2824 - acc: 0.9162 - val_loss: 1.0723 - val_acc: 0.7012\n",
      "Epoch 855/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2747 - acc: 0.8978 - val_loss: 0.9037 - val_acc: 0.7927\n",
      "Epoch 856/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2753 - acc: 0.9100 - val_loss: 0.8979 - val_acc: 0.7866\n",
      "Epoch 857/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2566 - acc: 0.9243 - val_loss: 0.9111 - val_acc: 0.7561\n",
      "Epoch 858/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3049 - acc: 0.8978 - val_loss: 1.1287 - val_acc: 0.6585\n",
      "Epoch 859/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.3221 - acc: 0.8793 - val_loss: 1.0588 - val_acc: 0.7317\n",
      "Epoch 860/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3012 - acc: 0.8957 - val_loss: 0.9723 - val_acc: 0.7439\n",
      "Epoch 861/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.2448 - acc: 0.9325 - val_loss: 0.9502 - val_acc: 0.7561\n",
      "Epoch 862/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2244 - acc: 0.9407 - val_loss: 1.0360 - val_acc: 0.7195\n",
      "Epoch 863/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2365 - acc: 0.9284 - val_loss: 1.0201 - val_acc: 0.7256\n",
      "Epoch 864/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2610 - acc: 0.9202 - val_loss: 0.9365 - val_acc: 0.7805\n",
      "Epoch 865/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2469 - acc: 0.9202 - val_loss: 1.0375 - val_acc: 0.7134\n",
      "Epoch 866/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.3302 - acc: 0.8875 - val_loss: 1.0587 - val_acc: 0.6768\n",
      "Epoch 867/1000\n",
      "489/489 [==============================] - 0s 175us/step - loss: 0.3278 - acc: 0.8896 - val_loss: 1.0637 - val_acc: 0.7012\n",
      "Epoch 868/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.3135 - acc: 0.8814 - val_loss: 1.0607 - val_acc: 0.7012\n",
      "Epoch 869/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2795 - acc: 0.8998 - val_loss: 0.9267 - val_acc: 0.7561\n",
      "Epoch 870/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.2480 - acc: 0.9162 - val_loss: 0.9498 - val_acc: 0.7622\n",
      "Epoch 871/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2539 - acc: 0.9202 - val_loss: 0.9557 - val_acc: 0.7683\n",
      "Epoch 872/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2322 - acc: 0.9346 - val_loss: 0.9284 - val_acc: 0.7439\n",
      "Epoch 873/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2610 - acc: 0.9121 - val_loss: 0.9097 - val_acc: 0.7866\n",
      "Epoch 874/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2448 - acc: 0.9264 - val_loss: 0.9511 - val_acc: 0.7744\n",
      "Epoch 875/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2758 - acc: 0.9121 - val_loss: 1.0341 - val_acc: 0.7195\n",
      "Epoch 876/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.3239 - acc: 0.8998 - val_loss: 1.0861 - val_acc: 0.7012\n",
      "Epoch 877/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.3114 - acc: 0.8814 - val_loss: 0.9523 - val_acc: 0.7317\n",
      "Epoch 878/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3232 - acc: 0.8978 - val_loss: 0.9709 - val_acc: 0.7073\n",
      "Epoch 879/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2916 - acc: 0.9162 - val_loss: 0.9009 - val_acc: 0.7805\n",
      "Epoch 880/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2706 - acc: 0.9141 - val_loss: 0.9131 - val_acc: 0.7683\n",
      "Epoch 881/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2538 - acc: 0.9141 - val_loss: 0.9075 - val_acc: 0.7866\n",
      "Epoch 882/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2524 - acc: 0.9264 - val_loss: 0.9119 - val_acc: 0.7500\n",
      "Epoch 883/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2707 - acc: 0.9162 - val_loss: 0.9044 - val_acc: 0.7744\n",
      "Epoch 884/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2458 - acc: 0.9387 - val_loss: 0.9257 - val_acc: 0.7744\n",
      "Epoch 885/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2241 - acc: 0.9427 - val_loss: 0.9042 - val_acc: 0.7561\n",
      "Epoch 886/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2459 - acc: 0.9223 - val_loss: 0.9721 - val_acc: 0.7561\n",
      "Epoch 887/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 149us/step - loss: 0.2498 - acc: 0.9182 - val_loss: 0.8865 - val_acc: 0.7744\n",
      "Epoch 888/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2605 - acc: 0.9366 - val_loss: 1.0038 - val_acc: 0.7439\n",
      "Epoch 889/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2560 - acc: 0.9223 - val_loss: 0.9222 - val_acc: 0.7927\n",
      "Epoch 890/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2729 - acc: 0.9121 - val_loss: 0.9164 - val_acc: 0.7622\n",
      "Epoch 891/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2535 - acc: 0.9325 - val_loss: 1.0651 - val_acc: 0.7073\n",
      "Epoch 892/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2742 - acc: 0.9100 - val_loss: 0.9450 - val_acc: 0.7561\n",
      "Epoch 893/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.2830 - acc: 0.9223 - val_loss: 0.9279 - val_acc: 0.7744\n",
      "Epoch 894/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2435 - acc: 0.9284 - val_loss: 1.1447 - val_acc: 0.7012\n",
      "Epoch 895/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2748 - acc: 0.9182 - val_loss: 1.3231 - val_acc: 0.6768\n",
      "Epoch 896/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.3213 - acc: 0.9121 - val_loss: 1.0237 - val_acc: 0.7073\n",
      "Epoch 897/1000\n",
      "489/489 [==============================] - 0s 135us/step - loss: 0.2922 - acc: 0.8978 - val_loss: 1.0056 - val_acc: 0.7317\n",
      "Epoch 898/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2700 - acc: 0.8978 - val_loss: 0.9885 - val_acc: 0.6951\n",
      "Epoch 899/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2656 - acc: 0.9039 - val_loss: 0.8942 - val_acc: 0.7683\n",
      "Epoch 900/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2397 - acc: 0.9284 - val_loss: 0.9250 - val_acc: 0.7622\n",
      "Epoch 901/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2098 - acc: 0.9468 - val_loss: 1.0603 - val_acc: 0.7195\n",
      "Epoch 902/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2254 - acc: 0.9427 - val_loss: 0.9755 - val_acc: 0.7317\n",
      "Epoch 903/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2258 - acc: 0.9305 - val_loss: 0.9425 - val_acc: 0.7561\n",
      "Epoch 904/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2240 - acc: 0.9366 - val_loss: 0.9972 - val_acc: 0.7561\n",
      "Epoch 905/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2369 - acc: 0.9243 - val_loss: 1.0819 - val_acc: 0.7195\n",
      "Epoch 906/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2235 - acc: 0.9325 - val_loss: 0.9596 - val_acc: 0.7622\n",
      "Epoch 907/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2566 - acc: 0.9202 - val_loss: 0.9776 - val_acc: 0.7744\n",
      "Epoch 908/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3017 - acc: 0.9039 - val_loss: 0.9597 - val_acc: 0.7195\n",
      "Epoch 909/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.2842 - acc: 0.8957 - val_loss: 0.9072 - val_acc: 0.7439\n",
      "Epoch 910/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2681 - acc: 0.9121 - val_loss: 0.9431 - val_acc: 0.7500\n",
      "Epoch 911/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2425 - acc: 0.9243 - val_loss: 1.0676 - val_acc: 0.7012\n",
      "Epoch 912/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2472 - acc: 0.9305 - val_loss: 0.8708 - val_acc: 0.7866\n",
      "Epoch 913/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2290 - acc: 0.9366 - val_loss: 0.8943 - val_acc: 0.7805\n",
      "Epoch 914/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.2780 - acc: 0.9039 - val_loss: 1.1439 - val_acc: 0.7073\n",
      "Epoch 915/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.2890 - acc: 0.8875 - val_loss: 0.9657 - val_acc: 0.7866\n",
      "Epoch 916/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.2760 - acc: 0.9018 - val_loss: 0.9890 - val_acc: 0.7500\n",
      "Epoch 917/1000\n",
      "489/489 [==============================] - 0s 169us/step - loss: 0.2557 - acc: 0.9182 - val_loss: 0.9365 - val_acc: 0.7927\n",
      "Epoch 918/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.2408 - acc: 0.9264 - val_loss: 1.0986 - val_acc: 0.7317\n",
      "Epoch 919/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2518 - acc: 0.9243 - val_loss: 0.9061 - val_acc: 0.7622\n",
      "Epoch 920/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2343 - acc: 0.9366 - val_loss: 0.9171 - val_acc: 0.7622\n",
      "Epoch 921/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2600 - acc: 0.9243 - val_loss: 1.0259 - val_acc: 0.7256\n",
      "Epoch 922/1000\n",
      "489/489 [==============================] - 0s 165us/step - loss: 0.2440 - acc: 0.9305 - val_loss: 1.0242 - val_acc: 0.6890\n",
      "Epoch 923/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.3550 - acc: 0.9039 - val_loss: 0.9566 - val_acc: 0.7439\n",
      "Epoch 924/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.3002 - acc: 0.8978 - val_loss: 0.9569 - val_acc: 0.6951\n",
      "Epoch 925/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2828 - acc: 0.9202 - val_loss: 0.9950 - val_acc: 0.7195\n",
      "Epoch 926/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2656 - acc: 0.9182 - val_loss: 0.9732 - val_acc: 0.7500\n",
      "Epoch 927/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.2402 - acc: 0.9346 - val_loss: 1.0022 - val_acc: 0.7134\n",
      "Epoch 928/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.3070 - acc: 0.9182 - val_loss: 0.9479 - val_acc: 0.7988\n",
      "Epoch 929/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2896 - acc: 0.9121 - val_loss: 0.9375 - val_acc: 0.7744\n",
      "Epoch 930/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2677 - acc: 0.9264 - val_loss: 1.1055 - val_acc: 0.6646\n",
      "Epoch 931/1000\n",
      "489/489 [==============================] - 0s 126us/step - loss: 0.2914 - acc: 0.9039 - val_loss: 0.9015 - val_acc: 0.7561\n",
      "Epoch 932/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2604 - acc: 0.9182 - val_loss: 0.8682 - val_acc: 0.7866\n",
      "Epoch 933/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2301 - acc: 0.9325 - val_loss: 0.9887 - val_acc: 0.7256\n",
      "Epoch 934/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.4299 - acc: 0.8916 - val_loss: 0.9575 - val_acc: 0.7317\n",
      "Epoch 935/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.3139 - acc: 0.8957 - val_loss: 0.8318 - val_acc: 0.7927\n",
      "Epoch 936/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2772 - acc: 0.9059 - val_loss: 0.9584 - val_acc: 0.7195\n",
      "Epoch 937/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2668 - acc: 0.9059 - val_loss: 0.9125 - val_acc: 0.7988\n",
      "Epoch 938/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2696 - acc: 0.9039 - val_loss: 0.8580 - val_acc: 0.7866\n",
      "Epoch 939/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2785 - acc: 0.9162 - val_loss: 0.9201 - val_acc: 0.7439\n",
      "Epoch 940/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2567 - acc: 0.9264 - val_loss: 0.8992 - val_acc: 0.7744\n",
      "Epoch 941/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2573 - acc: 0.9162 - val_loss: 1.0020 - val_acc: 0.7500\n",
      "Epoch 942/1000\n",
      "489/489 [==============================] - 0s 188us/step - loss: 0.2519 - acc: 0.9182 - val_loss: 0.9120 - val_acc: 0.7622\n",
      "Epoch 943/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2461 - acc: 0.9305 - val_loss: 1.0040 - val_acc: 0.7622\n",
      "Epoch 944/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2559 - acc: 0.9264 - val_loss: 0.8998 - val_acc: 0.7622\n",
      "Epoch 945/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.2409 - acc: 0.9387 - val_loss: 0.9656 - val_acc: 0.7500\n",
      "Epoch 946/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 0s 153us/step - loss: 0.2466 - acc: 0.9243 - val_loss: 0.9448 - val_acc: 0.7561\n",
      "Epoch 947/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.4312 - acc: 0.8732 - val_loss: 1.0846 - val_acc: 0.6463\n",
      "Epoch 948/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.3087 - acc: 0.8998 - val_loss: 0.8403 - val_acc: 0.7439\n",
      "Epoch 949/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.3099 - acc: 0.8916 - val_loss: 0.9061 - val_acc: 0.7073\n",
      "Epoch 950/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2629 - acc: 0.9059 - val_loss: 0.9116 - val_acc: 0.7683\n",
      "Epoch 951/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2643 - acc: 0.9121 - val_loss: 0.9086 - val_acc: 0.7439\n",
      "Epoch 952/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2422 - acc: 0.9223 - val_loss: 1.0314 - val_acc: 0.7317\n",
      "Epoch 953/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2354 - acc: 0.9264 - val_loss: 1.0386 - val_acc: 0.7195\n",
      "Epoch 954/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.3282 - acc: 0.9018 - val_loss: 0.8899 - val_acc: 0.7439\n",
      "Epoch 955/1000\n",
      "489/489 [==============================] - ETA: 0s - loss: 0.3008 - acc: 0.904 - 0s 141us/step - loss: 0.2997 - acc: 0.9059 - val_loss: 0.8948 - val_acc: 0.7805\n",
      "Epoch 956/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2319 - acc: 0.9346 - val_loss: 0.8631 - val_acc: 0.7622\n",
      "Epoch 957/1000\n",
      "489/489 [==============================] - 0s 173us/step - loss: 0.2210 - acc: 0.9366 - val_loss: 1.0205 - val_acc: 0.7012\n",
      "Epoch 958/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2563 - acc: 0.9243 - val_loss: 0.9696 - val_acc: 0.7561\n",
      "Epoch 959/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2300 - acc: 0.9346 - val_loss: 0.9100 - val_acc: 0.7500\n",
      "Epoch 960/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2329 - acc: 0.9284 - val_loss: 0.9067 - val_acc: 0.7805\n",
      "Epoch 961/1000\n",
      "489/489 [==============================] - 0s 159us/step - loss: 0.2565 - acc: 0.9162 - val_loss: 0.8672 - val_acc: 0.7927\n",
      "Epoch 962/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2639 - acc: 0.9141 - val_loss: 0.8987 - val_acc: 0.7744\n",
      "Epoch 963/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2312 - acc: 0.9366 - val_loss: 1.0245 - val_acc: 0.7195\n",
      "Epoch 964/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2332 - acc: 0.9387 - val_loss: 0.9913 - val_acc: 0.7256\n",
      "Epoch 965/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2158 - acc: 0.9366 - val_loss: 1.0263 - val_acc: 0.7195\n",
      "Epoch 966/1000\n",
      "489/489 [==============================] - 0s 131us/step - loss: 0.2168 - acc: 0.9305 - val_loss: 1.1619 - val_acc: 0.7134\n",
      "Epoch 967/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2359 - acc: 0.9264 - val_loss: 1.2095 - val_acc: 0.7134\n",
      "Epoch 968/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2392 - acc: 0.9325 - val_loss: 1.0737 - val_acc: 0.7317\n",
      "Epoch 969/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.2156 - acc: 0.9346 - val_loss: 1.1772 - val_acc: 0.7134\n",
      "Epoch 970/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2584 - acc: 0.9284 - val_loss: 1.0489 - val_acc: 0.6890\n",
      "Epoch 971/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2866 - acc: 0.9018 - val_loss: 0.9141 - val_acc: 0.7683\n",
      "Epoch 972/1000\n",
      "489/489 [==============================] - 0s 167us/step - loss: 0.2537 - acc: 0.9223 - val_loss: 0.9299 - val_acc: 0.7439\n",
      "Epoch 973/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2968 - acc: 0.9100 - val_loss: 1.0532 - val_acc: 0.7439\n",
      "Epoch 974/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3102 - acc: 0.8978 - val_loss: 1.1156 - val_acc: 0.6829\n",
      "Epoch 975/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3201 - acc: 0.8896 - val_loss: 0.9424 - val_acc: 0.7561\n",
      "Epoch 976/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2648 - acc: 0.9059 - val_loss: 1.1056 - val_acc: 0.7378\n",
      "Epoch 977/1000\n",
      "489/489 [==============================] - 0s 147us/step - loss: 0.2593 - acc: 0.9182 - val_loss: 1.0510 - val_acc: 0.7256\n",
      "Epoch 978/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2261 - acc: 0.9325 - val_loss: 0.9822 - val_acc: 0.7927\n",
      "Epoch 979/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2371 - acc: 0.9264 - val_loss: 1.0567 - val_acc: 0.7378\n",
      "Epoch 980/1000\n",
      "489/489 [==============================] - 0s 149us/step - loss: 0.3439 - acc: 0.9182 - val_loss: 0.9590 - val_acc: 0.7805\n",
      "Epoch 981/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2446 - acc: 0.9243 - val_loss: 0.9269 - val_acc: 0.7500\n",
      "Epoch 982/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2271 - acc: 0.9305 - val_loss: 1.0002 - val_acc: 0.7195\n",
      "Epoch 983/1000\n",
      "489/489 [==============================] - 0s 157us/step - loss: 0.2327 - acc: 0.9366 - val_loss: 1.0146 - val_acc: 0.7317\n",
      "Epoch 984/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2500 - acc: 0.9182 - val_loss: 0.9674 - val_acc: 0.7561\n",
      "Epoch 985/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2412 - acc: 0.9223 - val_loss: 0.9885 - val_acc: 0.7439\n",
      "Epoch 986/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2387 - acc: 0.9284 - val_loss: 1.2151 - val_acc: 0.6646\n",
      "Epoch 987/1000\n",
      "489/489 [==============================] - 0s 171us/step - loss: 0.2838 - acc: 0.8978 - val_loss: 0.9421 - val_acc: 0.7622\n",
      "Epoch 988/1000\n",
      "489/489 [==============================] - 0s 141us/step - loss: 0.2785 - acc: 0.9202 - val_loss: 1.0431 - val_acc: 0.7134\n",
      "Epoch 989/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2319 - acc: 0.9264 - val_loss: 0.9864 - val_acc: 0.7866\n",
      "Epoch 990/1000\n",
      "489/489 [==============================] - 0s 161us/step - loss: 0.2152 - acc: 0.9407 - val_loss: 0.9974 - val_acc: 0.7744\n",
      "Epoch 991/1000\n",
      "489/489 [==============================] - 0s 133us/step - loss: 0.2435 - acc: 0.9284 - val_loss: 1.0395 - val_acc: 0.7317\n",
      "Epoch 992/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2374 - acc: 0.9346 - val_loss: 1.1685 - val_acc: 0.6768\n",
      "Epoch 993/1000\n",
      "489/489 [==============================] - 0s 143us/step - loss: 0.2599 - acc: 0.9243 - val_loss: 0.9993 - val_acc: 0.7439\n",
      "Epoch 994/1000\n",
      "489/489 [==============================] - 0s 137us/step - loss: 0.2303 - acc: 0.9243 - val_loss: 1.0478 - val_acc: 0.7500\n",
      "Epoch 995/1000\n",
      "489/489 [==============================] - 0s 153us/step - loss: 0.2097 - acc: 0.9387 - val_loss: 0.9727 - val_acc: 0.7500\n",
      "Epoch 996/1000\n",
      "489/489 [==============================] - 0s 155us/step - loss: 0.2520 - acc: 0.9387 - val_loss: 1.1187 - val_acc: 0.7073\n",
      "Epoch 997/1000\n",
      "489/489 [==============================] - 0s 151us/step - loss: 0.2595 - acc: 0.9202 - val_loss: 0.9785 - val_acc: 0.7683\n",
      "Epoch 998/1000\n",
      "489/489 [==============================] - 0s 145us/step - loss: 0.2247 - acc: 0.9346 - val_loss: 1.0342 - val_acc: 0.7256\n",
      "Epoch 999/1000\n",
      "489/489 [==============================] - 0s 139us/step - loss: 0.2575 - acc: 0.9182 - val_loss: 1.0514 - val_acc: 0.7378\n",
      "Epoch 1000/1000\n",
      "489/489 [==============================] - 0s 128us/step - loss: 0.2248 - acc: 0.9407 - val_loss: 0.9666 - val_acc: 0.7866\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['acc'])\n",
    "history = model.fit(x_train,y_train,epochs=1000,validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24e771882e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24e7ce2dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('acc'),c='r',label=\"acc\")\n",
    "plt.plot(history.epoch,history.history.get('val_acc'),c='b',label=\"val_acc\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "164/164 [==============================] - 0s 122us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.96655676713803917, 0.78658536585365857]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:kr]",
   "language": "python",
   "name": "conda-env-kr-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
